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Location: Mumbai, Maharashtra, India

Sector: Technology, Information & Media

Company Size: 500 - 1,000 Employees

Employment: Full-Time, Permanent

Experience: 10 - 14 Years (Engineering Leadership)

Level: Engineering Manager / Group EM

ABOUT THIS MANDATE :

Recruiting Bond has been exclusively retained by one of India's most prominent and well-established digital platform organisations operating at the intersection of Technology, Information, and Media to identify and place an exceptional Engineering Manager who can lead engineering teams through an enterprise-wide AI adoption and digital transformation agenda.

This is a high-impact, hands-on leadership role at the nexus of people, product, and technology. The organisation is executing one of the most ambitious AI transformation programmes in its sector and this Engineering Manager will be a core driver of that change. You will lead multiple squads, own engineering delivery end-to-end, embed AI tooling and practices into the team's DNA, and shape the engineering culture of tomorrow.

We are seeking leaders who code when it matters, who build systems and teams with equal conviction, and who view AI not as a trend but as a fundamental shift in how great software is built.

THE OPPORTUNITY AT A GLANCE :

AI-First Engineering Culture :

• Own AI adoption across your squads - from LLM tooling integration to automation-first delivery workflows. Make AI a default, not an afterthought.

Hands-On Engineering Leadership :

• Stay close to the code. Lead architecture reviews, unblock engineers, and set the technical bar - not just the management agenda.

People & Org Builder :

• Grow engineers into leaders. Build squads of 615 across functions. Drive hiring, career frameworks, and a culture of psychological safety.

KEY RESPONSIBILITIES :

1. Hands-On Technical Engagement :

• Remain deeply embedded in the technical work participate in design reviews, architecture decisions, and critical code reviews
• Set and uphold the engineering quality bar : performance benchmarks, security standards, test coverage, and release quality
• Provide technical direction on backend platform strategy, API design, service decomposition, and data architecture
• Identify and resolve systemic technical debt and architectural risks across team-owned services
• Unblock engineers by diving into complex problems debugging, pair programming, and system analysis when it matters
• Own key technical decisions in collaboration with Tech Leads and Principal Engineers; balance pragmatism with long-term sustainability

2. AI Adoption, Integration & Transformation (2026 Mandate) :

• Define and execute the team's AI adoption roadmap - from developer tooling to product-facing AI features
• Champion the integration of GenAI tools (GitHub Copilot, Cursor, Claude, ChatGPT) across the full engineering workflow coding, testing, documentation, incident response
• Embed LLM-powered capabilities into the product : recommendation engines, intelligent search, conversational interfaces, content generation, and predictive systems
• Lead evaluation and adoption of AI-assisted SDLC practices : automated code review, AI-generated test suites, intelligent observability, and anomaly detection
• Partner with Data Science and ML Platform teams to productionise ML models with robust MLOps pipelines
• Build team literacy in prompt engineering, RAG (Retrieval-Augmented Generation), and AI agent frameworks
• Create an experimentation culture : run structured AI pilots, measure productivity impact, and scale what works
• Stay ahead of the AI tooling landscape and advise senior leadership on strategic AI investments and engineering implications

3. People Leadership & Team Development :

• Lead, manage, and grow squads of 6 - 15 engineers across seniority levels (L2 through L6 / Junior through Staff)
• Conduct structured 1 : 1s, career growth conversations, and development planning with every direct report
• Design and execute personalised AI upskilling programmes ensure every engineer develops practical AI fluency by end of 2026
• Build and maintain a high-performance team culture : clarity of ownership, accountability, fast feedback loops, and psychological safety
• Drive performance management fairly and rigorously recognise top performers, manage underperformance constructively
• Lead technical hiring end-to-end : define job requirements, conduct bar-raising interviews, and make data-driven hire decisions
• Contribute to engineering career frameworks and level definitions in partnership with the VP / Director of Engineering

4. Engineering Delivery & Execution Excellence :

• Own end-to-end delivery for multiple product squads from planning and scoping through production release and post-launch stability
• Implement and refine agile delivery frameworks (Scrum, Kanban, Shape Up) calibrated to squad needs and product cadence
• Drive predictable delivery : maintain healthy sprint velocity, manage WIP limits, and ensure dependency resolution across teams.
• Establish and own engineering KPIs : DORA metrics (deployment frequency, lead time, MTTR, change failure rate), uptime SLOs, and velocity trends
• Lead incident management : build blameless post-mortem culture, own RCA processes, and drive systemic reliability improvements
• Balance technical debt repayment with feature velocity negotiate prioritisation transparently with Product leadership

5. Strategic Leadership & Cross-Functional Influence :

• Serve as the primary engineering partner for Product, Design, Data, and Business stakeholders translate ambiguity into executable engineering plans
• Participate in quarterly roadmap planning, capacity forecasting, and OKR definition for engineering teams
• Represent engineering in leadership forums articulate technical constraints, risks, and opportunities in business terms
• Contribute to org-wide engineering strategy : platform investments, build-vs-buy decisions, and shared infrastructure priorities
• Build relationships across geographies (Mumbai HQ + distributed teams) to maintain alignment and delivery cohesion
• Act as a culture carrier and ambassador for engineering excellence, innovation, and responsible AI use

AI TRANSFORMATION LEADERSHIP 2026 EXPECTATIONS :

In 2026, Engineering Managers at this organisation are expected to be active architects of AI transformation not passive observers. The following outlines the specific AI leadership expectations for this role :

AI Developer Productivity

• Drive measurable uplift in developer velocity through AI tooling adoption. Target : 30%+ reduction in code review cycle time and 40%+ increase in test coverage automation by Q3 2026.

LLM & GenAI Product Features

• Own delivery of GenAI-powered product capabilities : intelligent content, semantic search, personalisation, and conversational UX in production, at scale.

AI-Augmented Observability

• Implement AI-driven monitoring and anomaly detection pipelines. Reduce MTTR by leveraging predictive alerting, intelligent runbooks, and auto-remediation scripts.

Team AI Fluency :

• Build mandatory AI literacy across all engineering levels.
• Every engineer understands prompt engineering basics, AI ethics guardrails, and responsible AI deployment practices.

Responsible AI Governance :

• Partner with Security, Legal, and Data Privacy to ensure all AI deployments meet compliance standards, bias mitigation requirements, and explainability benchmarks.

TECHNOLOGY STACK & DOMAIN FAMILIARITY REQUIRED :

• Languages: Java/ Go/ Python/ Node.js /PHP /Rust (must be hands-on in at least 2)
• Cloud: AWS / GCP / Azure (multi-cloud exposure strongly preferred)
• AI & GenAI: OpenAI / Anthropic / Gemini APIs /LangChain /LlamaIndex / RAG / Vector DBs / GitHub
• Copilot: Cursor /Hugging Face
• Containers: Docker /Kubernetes /Helm /Service Mesh (Istio / Linkerd)
• Databases: PostgreSQL /MongoDB / Redis / Cassandra / Elasticsearch / Pinecone (Vector DB)
• Messaging: Apache Kafka /RabbitMQ /AWS SQS/SNS /Google Pub/Sub
• MLOps & DataOps: MLflow /Kubeflow / SageMaker / Vertex AI /Airflow /dbt
• Observability: Datadog /Prometheus /Grafana /OpenTelemetry / Jaeger /ELK Stack
• CI/CD & IaC: GitHub Actions ArgoCD / Jenkins / Terraform /Ansible /Backstage (IDP)

QUALIFICATIONS & CANDIDATE PROFILE :

Education :

• B.E. / B.Tech or M.E. / M.Tech from a Tier-I or Tier-II Institution - CS, IS, ECE, AI/ML streams strongly preferred
• Demonstrated engineering depth and leadership impact may complement institution pedigree

Experience :

• 10 to 14 years of progressive engineering experience, with at least 3 years in a formal Engineering Manager or equivalent people-leadership role
• Proven track record of managing and scaling engineering teams (615+ engineers) in a fast-growing SaaS or digital product environment
• Hands-on backend engineering background must be able to read, write, and critique production code
• Direct experience driving AI/ML feature delivery or AI tooling adoption within engineering organisations
• Exposure across start-up, mid-size, and large-scale product organisations, preferred adaptability is a core requirement
• Strong CS fundamentals: distributed systems, algorithms, system design, and software architecture
• Demonstrated career stability minimum of 2 years of average tenure per organisation.

The Ideal Engineering Manager in 2026 :

• Leads with context, not control, empowers engineers while maintaining accountability and quality
• Is fluent in both people language and technical language, switches registers naturally with engineers and executives alike
• Sees AI as a force multiplier for the team, not a threat. Actively experiments with and advocates for AI tooling
• Measures success by team outcomes, not personal output. Takes pride in what the team ships, not what they build alone
• Creates feedback loops obsessively between product and engineering, between seniors and juniors, between metrics and decisions
• Has strong opinions, loosely held, brings conviction to discussions but updates on evidence
• Invests in engineering excellence as seriously as delivery velocity knows that quality and speed are not opposites

WHY THIS ROLE STANDS APART :

AI Transformation at Scale :

• Lead one of the most significant AI adoption programmes in India's digital media sector.
• Our decisions will shape how hundreds of engineers work in 2026 and beyond.

Hands-On & Strategic Balance :

• A rare EM role that actively encourages technical depth.
• Stay close to the code while owning the people agenda - the best of both worlds.

Established Platform, Real Scale :

• 5001,000 engineers, proven product-market fit, and the org maturity to execute.
• This is not a greenfield startup gamble it is a serious company with serious ambition.

Clear Leadership Growth Path :

• A visible, direct path toward Director / VP of Engineering.
• Senior leadership is invested in growing its next generation of technology executives.

Review Criteria

• Strong Data Scientist/Machine Learnings/ AI Engineer Profile
• 2+ years of hands-on experience as a Data Scientist or Machine Learning Engineer building ML models
• Strong expertise in Python with the ability to implement classical ML algorithms including linear regression, logistic regression, decision trees, gradient boosting, etc.
• Hands-on experience in minimum 2+ usecaseds out of recommendation systems, image data, fraud/risk detection, price modelling, propensity models
• Strong exposure to NLP, including text generation or text classification (Text G), embeddings, similarity models, user profiling, and feature extraction from unstructured text
• Experience productionizing ML models through APIs/CI/CD/Docker and working on AWS or GCP environments
• Preferred (Company) – Must be from product companies

Job Specific Criteria

• CV Attachment is mandatory
• What's your current company?
• Which use cases you have hands on experience?
• Are you ok for Mumbai location (if candidate is from outside Mumbai)?
• Reason for change (if candidate has been in current company for less than 1 year)?
• Reason for hike (if greater than 25%)?

Role & Responsibilities

• Partner with Product to spot high-leverage ML opportunities tied to business metrics.
• Wrangle large structured and unstructured datasets; build reliable features and data contracts.
• Build and ship models to:
• Enhance customer experiences and personalization
• Boost revenue via pricing/discount optimization
• Power user-to-user discovery and ranking (matchmaking at scale)
• Detect and block fraud/risk in real time
• Score conversion/churn/acceptance propensity for targeted actions
• Collaborate with Engineering to productionize via APIs/CI/CD/Docker on AWS.
• Design and run A/B tests with guardrails.
• Build monitoring for model/data drift and business KPIs

Ideal Candidate

• 2–5 years of DS/ML experience in consumer internet / B2C products, with 7–8 models shipped to production end-to-end.
• Proven, hands-on success in at least two (preferably 3–4) of the following:
• Recommender systems (retrieval + ranking, NDCG/Recall, online lift; bandits a plus)
• Fraud/risk detection (severe class imbalance, PR-AUC)
• Pricing models (elasticity, demand curves, margin vs. win-rate trade-offs, guardrails/simulation)
• Propensity models (payment/churn)
• Programming: strong Python and SQL; solid git, Docker, CI/CD.
• Cloud and data: experience with AWS or GCP; familiarity with warehouses/dashboards (Redshift/BigQuery, Looker/Tableau).
• ML breadth: recommender systems, NLP or user profiling, anomaly detection.
• Communication: clear storytelling with data; can align stakeholders and drive decisions.

ROLES AND RESPONSIBILITIES:

You will be responsible for architecting, implementing, and optimizing Dremio-based data Lakehouse environments integrated with cloud storage, BI, and data engineering ecosystems. The role requires a strong balance of architecture design, data modeling, query optimization, and governance enablement in large-scale analytical environments.

• Design and implement Dremio lakehouse architecture on cloud (AWS/Azure/Snowflake/Databricks ecosystem).
• Define data ingestion, curation, and semantic modeling strategies to support analytics and AI workloads.
• Optimize Dremio reflections, caching, and query performance for diverse data consumption patterns.
• Collaborate with data engineering teams to integrate data sources via APIs, JDBC, Delta/Parquet, and object storage layers (S3/ADLS).
• Establish best practices for data security, lineage, and access control aligned with enterprise governance policies.
• Support self-service analytics by enabling governed data products and semantic layers.
• Develop reusable design patterns, documentation, and standards for Dremio deployment, monitoring, and scaling.
• Work closely with BI and data science teams to ensure fast, reliable, and well-modeled access to enterprise data.

IDEAL CANDIDATE:

• Bachelor’s or Master’s in Computer Science, Information Systems, or related field.
• 5+ years in data architecture and engineering, with 3+ years in Dremio or modern lakehouse platforms.
• Strong expertise in SQL optimization, data modeling, and performance tuning within Dremio or similar query engines (Presto, Trino, Athena).
• Hands-on experience with cloud storage (S3, ADLS, GCS), Parquet/Delta/Iceberg formats, and distributed query planning.
• Knowledge of data integration tools and pipelines (Airflow, DBT, Kafka, Spark, etc.).
• Familiarity with enterprise data governance, metadata management, and role-based access control (RBAC).
• Excellent problem-solving, documentation, and stakeholder communication skills.

PREFERRED:

• Experience integrating Dremio with BI tools (Tableau, Power BI, Looker) and data catalogs (Collibra, Alation, Purview).
• Exposure to Snowflake, Databricks, or BigQuery environments.
• Experience in high-tech, manufacturing, or enterprise data modernization programs.

Review Criteria

• Strong Data Scientist/Machine Learnings/ AI Engineer Profile
• 2+ years of hands-on experience as a Data Scientist or Machine Learning Engineer building ML models
• Strong expertise in Python with the ability to implement classical ML algorithms including linear regression, logistic regression, decision trees, gradient boosting, etc.
• Hands-on experience in minimum 2+ usecaseds out of recommendation systems, image data, fraud/risk detection, price modelling, propensity models
• Strong exposure to NLP, including text generation or text classification (Text G), embeddings, similarity models, user profiling, and feature extraction from unstructured text
• Experience productionizing ML models through APIs/CI/CD/Docker and working on AWS or GCP environments
• Preferred (Company) – Must be from product companies

Job Specific Criteria

• CV Attachment is mandatory
• What's your current company?
• Which use cases you have hands on experience?
• Are you ok for Mumbai location (if candidate is from outside Mumbai)?
• Reason for change (if candidate has been in current company for less than 1 year)?
• Reason for hike (if greater than 25%)?

Role & Responsibilities

• Partner with Product to spot high-leverage ML opportunities tied to business metrics.
• Wrangle large structured and unstructured datasets; build reliable features and data contracts.
• Build and ship models to:
• Enhance customer experiences and personalization
• Boost revenue via pricing/discount optimization
• Power user-to-user discovery and ranking (matchmaking at scale)
• Detect and block fraud/risk in real time
• Score conversion/churn/acceptance propensity for targeted actions
• Collaborate with Engineering to productionize via APIs/CI/CD/Docker on AWS.
• Design and run A/B tests with guardrails.
• Build monitoring for model/data drift and business KPIs

Ideal Candidate

• 2–5 years of DS/ML experience in consumer internet / B2C products, with 7–8 models shipped to production end-to-end.
• Proven, hands-on success in at least two (preferably 3–4) of the following:
• Recommender systems (retrieval + ranking, NDCG/Recall, online lift; bandits a plus)
• Fraud/risk detection (severe class imbalance, PR-AUC)
• Pricing models (elasticity, demand curves, margin vs. win-rate trade-offs, guardrails/simulation)
• Propensity models (payment/churn)
• Programming: strong Python and SQL; solid git, Docker, CI/CD.
• Cloud and data: experience with AWS or GCP; familiarity with warehouses/dashboards (Redshift/BigQuery, Looker/Tableau).
• ML breadth: recommender systems, NLP or user profiling, anomaly detection.
• Communication: clear storytelling with data; can align stakeholders and drive decisions.

Position: AWS Data Engineer

Experience: 5 to 7 Years

Location: Bengaluru, Pune, Chennai, Mumbai, Gurugram

Work Mode: Hybrid (3 days work from office per week)

Employment Type: Full-time

About the Role:

We are seeking a highly skilled and motivated AWS Data Engineer with 5–7 years of experience in building and optimizing data pipelines, architectures, and data sets. The ideal candidate will have strong experience with AWS services including Glue, Athena, Redshift, Lambda, DMS, RDS, and CloudFormation. You will be responsible for managing the full data lifecycle from ingestion to transformation and storage, ensuring efficiency and performance.

Key Responsibilities:

• Design, develop, and optimize scalable ETL pipelines using AWS Glue, Python/PySpark, and SQL.
• Work extensively with AWS services such as Glue, Athena, Lambda, DMS, RDS, Redshift, CloudFormation, and other serverless technologies.
• Implement and manage data lake and warehouse solutions using AWS Redshift and S3.
• Optimize data models and storage for cost-efficiency and performance.
• Write advanced SQL queries to support complex data analysis and reporting requirements.
• Collaborate with stakeholders to understand data requirements and translate them into scalable solutions.
• Ensure high data quality and integrity across platforms and processes.
• Implement CI/CD pipelines and best practices for infrastructure as code using CloudFormation or similar tools.

Required Skills & Experience:

• Strong hands-on experience with Python or PySpark for data processing.
• Deep knowledge of AWS Glue, Athena, Lambda, Redshift, RDS, DMS, and CloudFormation.
• Proficiency in writing complex SQL queries and optimizing them for performance.
• Familiarity with serverless architectures and AWS best practices.
• Experience in designing and maintaining robust data architectures and data lakes.
• Ability to troubleshoot and resolve data pipeline issues efficiently.
• Strong communication and stakeholder management skills.

Position: AWS Data Engineer

Experience: 5 to 7 Years

Location: Bengaluru, Pune, Chennai, Mumbai, Gurugram

Work Mode: Hybrid (3 days work from office per week)

Employment Type: Full-time

About the Role:

We are seeking a highly skilled and motivated AWS Data Engineer with 5–7 years of experience in building and optimizing data pipelines, architectures, and data sets. The ideal candidate will have strong experience with AWS services including Glue, Athena, Redshift, Lambda, DMS, RDS, and CloudFormation. You will be responsible for managing the full data lifecycle from ingestion to transformation and storage, ensuring efficiency and performance.

Key Responsibilities:

• Design, develop, and optimize scalable ETL pipelines using AWS Glue, Python/PySpark, and SQL.
• Work extensively with AWS services such as Glue, Athena, Lambda, DMS, RDS, Redshift, CloudFormation, and other serverless technologies.
• Implement and manage data lake and warehouse solutions using AWS Redshift and S3.
• Optimize data models and storage for cost-efficiency and performance.
• Write advanced SQL queries to support complex data analysis and reporting requirements.
• Collaborate with stakeholders to understand data requirements and translate them into scalable solutions.
• Ensure high data quality and integrity across platforms and processes.
• Implement CI/CD pipelines and best practices for infrastructure as code using CloudFormation or similar tools.

Required Skills & Experience:

• Strong hands-on experience with Python or PySpark for data processing.
• Deep knowledge of AWS Glue, Athena, Lambda, Redshift, RDS, DMS, and CloudFormation.
• Proficiency in writing complex SQL queries and optimizing them for performance.
• Familiarity with serverless architectures and AWS best practices.
• Experience in designing and maintaining robust data architectures and data lakes.
• Ability to troubleshoot and resolve data pipeline issues efficiently.
• Strong communication and stakeholder management skills.

Job Overview:

We are seeking an experienced AWS Data Engineer to join our growing data team. The ideal candidate will have hands-on experience with AWS Glue, Redshift, PySpark, and other AWS services to build robust, scalable data pipelines. This role is perfect for someone passionate about data engineering, automation, and cloud-native development.

Key Responsibilities:

• Design, build, and maintain scalable and efficient ETL pipelines using AWS Glue, PySpark, and related tools.
• Integrate data from diverse sources and ensure its quality, consistency, and reliability.
• Work with large datasets in structured and semi-structured formats across cloud-based data lakes and warehouses.
• Optimize and maintain data infrastructure, including Amazon Redshift, for high performance.
• Collaborate with data analysts, data scientists, and product teams to understand data requirements and deliver solutions.
• Automate data validation, transformation, and loading processes to support real-time and batch data processing.
• Monitor and troubleshoot data pipeline issues and ensure smooth operations in production environments.

Required Skills:

• 5 to 7 years of hands-on experience in data engineering roles.
• Strong proficiency in Python and PySpark for data transformation and scripting.
• Deep understanding and practical experience with AWS Glue, AWS Redshift, S3, and other AWS data services.
• Solid understanding of SQL and database optimization techniques.
• Experience working with large-scale data pipelines and high-volume data environments.
• Good knowledge of data modeling, warehousing, and performance tuning.

Preferred/Good to Have:

• Experience with workflow orchestration tools like Airflow or Step Functions.
• Familiarity with CI/CD for data pipelines.
• Knowledge of data governance and security best practices on AWS.

Job Description: 

Please find below details:

Experience - 5+ Years

Location- Bangalore/Python

Role Overview

We are seeking a skilled Python Data Engineer with expertise in designing and implementing data solutions using the AWS cloud platform. The ideal candidate will be responsible for building and maintaining scalable, efficient, and secure data pipelines while leveraging Python and AWS services to enable robust data analytics and decision-making processes.

Key Responsibilities

• Design, develop, and optimize data pipelines using Python and AWS services such as Glue, Lambda, S3, EMR, Redshift, Athena, and Kinesis.
• Implement ETL/ELT processes to extract, transform, and load data from various sources into centralized repositories (e.g., data lakes or data warehouses).
• Collaborate with cross-functional teams to understand business requirements and translate them into scalable data solutions.
• Monitor, troubleshoot, and enhance data workflows for performance and cost optimization.
• Ensure data quality and consistency by implementing validation and governance practices.
• Work on data security best practices in compliance with organizational policies and regulations.
• Automate repetitive data engineering tasks using Python scripts and frameworks.
• Leverage CI/CD pipelines for deployment of data workflows on AWS.

Required Skills and Qualifications

• Professional Experience: 5+ years of experience in data engineering or a related field.
• Programming: Strong proficiency in Python, with experience in libraries like pandas, pySpark, or boto3.
• AWS Expertise: Hands-on experience with core AWS services for data engineering, such as:
• AWS Glue for ETL/ELT.
• S3 for storage.
• Redshift or Athena for data warehousing and querying.
• Lambda for serverless compute.
• Kinesis or SNS/SQS for data streaming.
• IAM Roles for security.
• Databases: Proficiency in SQL and experience with relational (e.g., PostgreSQL, MySQL) and NoSQL (e.g., DynamoDB) databases.
• Data Processing: Knowledge of big data frameworks (e.g., Hadoop, Spark) is a plus.
• DevOps: Familiarity with CI/CD pipelines and tools like Jenkins, Git, and CodePipeline.
• Version Control: Proficient with Git-based workflows.
• Problem Solving: Excellent analytical and debugging skills.

Optional Skills

• Knowledge of data modeling and data warehouse design principles.
• Experience with data visualization tools (e.g., Tableau, Power BI).
• Familiarity with containerization (e.g., Docker) and orchestration (e.g., Kubernetes).
• Exposure to other programming languages like Scala or Java.

Dear Candidate,

We are urgently Hiring AWS Cloud Engineer for Bangalore Location.

Position: AWS Cloud Engineer

Location: Bangalore

Experience: 8-11 yrs

Skills: Aws Cloud

Salary: Best in Industry (20-25% Hike on the current ctc)

Note:

only Immediate to 15 days Joiners will be preferred.

Candidates from Tier 1 companies will only be shortlisted and selected

Candidates' NP more than 30 days will get rejected while screening.

Offer shoppers will be rejected.

Job description:

Description:

Title: AWS Cloud Engineer

Prefer BLR / HYD – else any location is fine

Work Mode: Hybrid – based on HR rule (currently 1 day per month)

Shift Timings 24 x 7 (Work in shifts on rotational basis)

Total Experience in Years- 8+ yrs, 5 yrs of relevant exp is required.

Must have- AWS platform, Terraform, Redshift / Snowflake, Python / Shell Scripting

Experience and Skills Requirements:

Experience:

8 years of experience in a technical role working with AWS

Mandatory

Technical troubleshooting and problem solving

AWS management of large-scale IaaS PaaS solutions

Cloud networking and security fundamentals

Experience using containerization in AWS

Working Data warehouse knowledge Redshift and Snowflake preferred

Working with IaC – Terraform and Cloud Formation

Working understanding of scripting languages including Python and Shell

Collaboration and communication skills

Highly adaptable to changes in a technical environment

Optional

Experience using monitoring and observer ability toolsets inc. Splunk, Datadog

Experience using Github Actions

Experience using AWS RDS/SQL based solutions

Experience working with streaming technologies inc. Kafka, Apache Flink

Experience working with a ETL environments

Experience working with a confluent cloud platform

Certifications:

Minimum

AWS Certified SysOps Administrator – Associate

AWS Certified DevOps Engineer - Professional

Preferred

AWS Certified Solutions Architect – Associate

Responsibilities:

Responsible for technical delivery of managed services across NTT Data customer account base. Working as part of a team providing a Shared Managed Service.

The following is a list of expected responsibilities:

To manage and support a customer’s AWS platform

To be technical hands on

Provide Incident and Problem management on the AWS IaaS and PaaS Platform

Involvement in the resolution or high priority Incidents and problems in an efficient and timely manner

Actively monitor an AWS platform for technical issues

To be involved in the resolution of technical incidents tickets

Assist in the root cause analysis of incidents

Assist with improving efficiency and processes within the team

Examining traces and logs

Working with third party suppliers and AWS to jointly resolve incidents

Good to have:

Confluent Cloud

Snowflake

Best Regards,

Minakshi Soni

Executive - Talent Acquisition (L2)

Rigel Networks

Worldwide Locations: USA | HK | IN 

Technical Skills:

• Ability to understand and translate business requirements into design.
• Proficient in AWS infrastructure components such as S3, IAM, VPC, EC2, and Redshift.
• Experience in creating ETL jobs using Python/PySpark.
• Proficiency in creating AWS Lambda functions for event-based jobs.
• Knowledge of automating ETL processes using AWS Step Functions.
• Competence in building data warehouses and loading data into them.

Responsibilities:

• Understand business requirements and translate them into design.
• Assess AWS infrastructure needs for development work.
• Develop ETL jobs using Python/PySpark to meet requirements.
• Implement AWS Lambda for event-based tasks.
• Automate ETL processes using AWS Step Functions.
• Build data warehouses and manage data loading.
• Engage with customers and stakeholders to articulate the benefits of proposed solutions and frameworks.

DATA ENGINEER

Overview

They started with a singular belief - what is beautiful cannot and should not be defined in marketing meetings. It's defined by the regular people like us, our sisters, our next-door neighbours, and the friends we make on the playground and in lecture halls. That's why we stand for people-proving everything we do. From the inception of a product idea to testing the final formulations before launch, our consumers are a part of each and every process. They guide and inspire us by sharing their stories with us. They tell us not only about the product they need and the skincare issues they face but also the tales of their struggles, dreams and triumphs. Skincare goes deeper than skin. It's a form of self-care for many. Wherever someone is on this journey, we want to cheer them on through the products we make, the content we create and the conversations we have. What we wish to build is more than a brand. We want to build a community that grows and glows together - cheering each other on, sharing knowledge, and ensuring people always have access to skincare that really works.

Job Description:

We are seeking a skilled and motivated Data Engineer to join our team. As a Data Engineer, you will be responsible for designing, developing, and maintaining the data infrastructure and systems that enable efficient data collection, storage, processing, and analysis. You will collaborate with cross-functional teams, including data scientists, analysts, and software engineers, to implement data pipelines and ensure the availability, reliability, and scalability of our data platform.

Responsibilities:

Design and implement scalable and robust data pipelines to collect, process, and store data from various sources.

Develop and maintain data warehouse and ETL (Extract, Transform, Load) processes for data integration and transformation.

Optimize and tune the performance of data systems to ensure efficient data processing and analysis.

Collaborate with data scientists and analysts to understand data requirements and implement solutions for data modeling and analysis.

Identify and resolve data quality issues, ensuring data accuracy, consistency, and completeness.

Implement and maintain data governance and security measures to protect sensitive data.

Monitor and troubleshoot data infrastructure, perform root cause analysis, and implement necessary fixes.

Stay up-to-date with emerging technologies and industry trends in data engineering and recommend their adoption when appropriate.

Qualifications:

Bachelor’s or higher degree in Computer Science, Information Systems, or a related field.

Proven experience as a Data Engineer or similar role, working with large-scale data processing and storage systems.

Strong programming skills in languages such as Python, Java, or Scala.

Experience with big data technologies and frameworks like Hadoop, Spark, or Kafka.

Proficiency in SQL and database management systems (e.g., MySQL, PostgreSQL, or Oracle).

Familiarity with cloud platforms like AWS, Azure, or GCP, and their data services (e.g., S3, Redshift, BigQuery).

Solid understanding of data modeling, data warehousing, and ETL principles.

Knowledge of data integration techniques and tools (e.g., Apache Nifi, Talend, or Informatica).

Strong problem-solving and analytical skills, with the ability to handle complex data challenges.

Excellent communication and collaboration skills to work effectively in a team environment.

Preferred Qualifications:

Advanced knowledge of distributed computing and parallel processing.

Experience with real-time data processing and streaming technologies (e.g., Apache Kafka, Apache Flink).

Familiarity with machine learning concepts and frameworks (e.g., TensorFlow, PyTorch).

Knowledge of containerization and orchestration technologies (e.g., Docker, Kubernetes).

Experience with data visualization and reporting tools (e.g., Tableau, Power BI).

Certification in relevant technologies or data engineering disciplines.

Roles and

Responsibilities

Seeking AWS Cloud Engineer /Data Warehouse Developer for our Data CoE team to

help us in configure and develop new AWS environments for our Enterprise Data Lake,

migrate the on-premise traditional workloads to cloud. Must have a sound

understanding of BI best practices, relational structures, dimensional data modelling,

structured query language (SQL) skills, data warehouse and reporting techniques.

 Extensive experience in providing AWS Cloud solutions to various business

use cases.

 Creating star schema data models, performing ETLs and validating results with

business representatives

 Supporting implemented BI solutions by: monitoring and tuning queries and

data loads, addressing user questions concerning data integrity, monitoring

performance and communicating functional and technical issues.

Job Description: -

This position is responsible for the successful delivery of business intelligence

information to the entire organization and is experienced in BI development and

implementations, data architecture and data warehousing.

Requisite Qualification

Essential

-

AWS Certified Database Specialty or -

AWS Certified Data Analytics

Preferred

Any other Data Engineer Certification

Requisite Experience

Essential 4 -7 yrs of experience

Preferred 2+ yrs of experience in ETL & data pipelines

Skills Required

Special Skills Required

 AWS: S3, DMS, Redshift, EC2, VPC, Lambda, Delta Lake, CloudWatch etc.

 Bigdata: Databricks, Spark, Glue and Athena

 Expertise in Lake Formation, Python programming, Spark, Shell scripting

 Minimum Bachelor’s degree with 5+ years of experience in designing, building,

and maintaining AWS data components

 3+ years of experience in data component configuration, related roles and

access setup

 Expertise in Python programming

 Knowledge in all aspects of DevOps (source control, continuous integration,

deployments, etc.)

 Comfortable working with DevOps: Jenkins, Bitbucket, CI/CD

 Hands on ETL development experience, preferably using or SSIS

 SQL Server experience required

 Strong analytical skills to solve and model complex business requirements

 Sound understanding of BI Best Practices/Methodologies, relational structures,

dimensional data modelling, structured query language (SQL) skills, data

warehouse and reporting techniques

Preferred Skills

Required

 Experience working in the SCRUM Environment.

 Experience in Administration (Windows/Unix/Network/Database/Hadoop) is a

plus.

 Experience in SQL Server, SSIS, SSAS, SSRS

 Comfortable with creating data models and visualization using Power BI

 Hands on experience in relational and multi-dimensional data modelling,

including multiple source systems from databases and flat files, and the use of

standard data modelling tools

 Ability to collaborate on a team with infrastructure, BI report development and

business analyst resources, and clearly communicate solutions to both

technical and non-technical team members