Databricks Engineer with AI/BI at TECHNAVITAS INFO INDIA PVT LTD · Hyderabad · 5 - 8 years · ₹5L - ₹20L / yr · Bootstrapped · Posted 14 May 2026
PythonData Engineer — AI / BI
Artificial Intelligence & Business Intelligence | Data & Analytics
Who We Are:
Since our inception back in 2006, Navitas has grown to be an industry leader in the digital transformation space, and we’ve served as trusted advisors supporting our client base within the commercial, federal, and state and local markets.
What We Do:
At our very core, we’re a group of problem solvers providing our award-winning technology solutions to drive digital acceleration for our customers! With proven solutions, award-winning technologies, and a team of expert problem solvers, Navitas has consistently empowered customers to use technology as a competitive advantage and deliver cutting-edge transformative solutions.
Position Overview
We are seeking a Databricks Engineer to design, build, and operate a Data & AI platform with a strong foundation in the Medallion Architecture (raw/bronze, curated/silver, and mart/gold layers). This platform will orchestrate complex data workflows and scalable ELT pipelines to integrate data from enterprise systems such as PeopleSoft, D2L, and Salesforce, delivering high-quality, governed data for machine learning, AI/BI, and analytics at scale.
You will play a critical role in engineering the infrastructure and workflows that enable seamless data flow across the enterprise, ensure operational excellence, and provide the backbone for strategic decision-making, predictive modeling, and innovation
Responsibilities:
Data & AI Platform Engineering (Databricks-Centric):
- Design, implement, and optimize end-to-end data pipelines on Databricks, following the Medallion Architecture principles.
- Build robust and scalable ETL/ELT pipelines using Apache Spark and Delta Lake to transform raw (bronze) data into trusted curated (silver) and analytics-ready (gold) data layers.
- Operationalize Databricks Workflows for orchestration, dependency management, and pipeline automation.
- Apply schema evolution and data versioning to support agile data development.
Platform Integration & Data Ingestion:
- Connect and ingest data from enterprise systems such as PeopleSoft, D2L, and Salesforce using APIs, JDBC, or other integration frameworks.
- Implement connectors and ingestion frameworks that accommodate structured, semi-structured, and unstructured data.
- Design standardized data ingestion processes with automated error handling, retries, and alerting.
Data Quality, Monitoring, and Governance:
- Develop data quality checks, validation rules, and anomaly detection mechanisms to ensure data integrity across all layers.
- Integrate monitoring and observability tools (e.g., Databricks metrics, Grafana) to track ETL performance, latency, and failures.
- Implement Unity Catalog or equivalent tools for centralized metadata management, data lineage, and governance policy enforcement.
Security, Privacy, and Compliance:
- Enforce data security best practices including row-level security, encryption at rest/in transit, and fine-grained access control via Unity Catalog.
- Design and implement data masking, tokenization, and anonymization for compliance with privacy regulations (e.g., GDPR, FERPA).
- Work with security teams to audit and certify compliance controls.
AI/ML-Ready Data Foundation:
- Enable data scientists by delivering high-quality, feature-rich data sets for model training and inference.
- Support AIOps/MLOps lifecycle workflows using MLflow for experiment tracking, model registry, and deployment within Databricks.
- Collaborate with AI/ML teams to create reusable feature stores and training pipelines.
Cloud Data Architecture and Storage:
- Architect and manage data lakes on Azure Data Lake Storage (ADLS) or Amazon S3, and design ingestion pipelines to feed the bronze layer.
- Build data marts and warehousing solutions using platforms like Databricks.
- Optimize data storage and access patterns for performance and cost-efficiency.
Documentation & Enablement:
- Maintain technical documentation, architecture diagrams, data dictionaries, and runbooks for all pipelines and components.
- Provide training and enablement sessions to internal stakeholders on the Databricks platform, Medallion Architecture, and data governance practices.
- Conduct code reviews and promote reusable patterns and frameworks across teams.
Reporting and Accountability:
- Submit a weekly schedule of hours worked and progress reports outlining completed tasks, upcoming plans, and blockers.
- Track deliverables against roadmap milestones and communicate risks or dependencies.
Required Qualifications:
- Hands-on experience with Databricks, Delta Lake, and Apache Spark for large-scale data engineering.
- Deep understanding of ELT pipeline development, orchestration, and monitoring in cloud-native environments.
- Experience implementing Medallion Architecture (Bronze/Silver/Gold) and working with data versioning and schema enforcement in enterprise grade environments.
- Strong proficiency in SQL, Python, or Scala for data transformations and workflow logic.
- Proven experience integrating enterprise platforms (e.g., PeopleSoft, Salesforce, D2L) into centralized data platforms.
- Familiarity with data governance, lineage tracking, and metadata management tools.
Preferred Qualifications:
- Prior UMGC or USM experience preferred.
- Experience with Databricks Unity Catalog for metadata management and access control.
- Experience deploying ML models at scale using MLFlow or similar MLOps tools.
- Familiarity with cloud platforms like Azure or AWS, including storage, security, and networking aspects.
- Knowledge of data warehouse design and star/snowflake schema modeling.
Equal Employer/Veterans/Disabled
Navitas Business Consulting is an affirmative action and equal opportunity employer. If reasonable accommodation is needed to participate in the job application or interview process, to perform essential job functions, and/or to receive other benefits and privileges of employment, please contact Navitas Human Resources.
Navitas is an equal opportunity employer. We provide employment and opportunities for advancement, compensation, training, and growth according to individual merit, without regard to race, color, religion, sex (including pregnancy), national origin, sexual orientation, gender identity or expression, marital status, age, genetic information, disability, veteran-status veteran or military status, or any other characteristic protected under applicable Federal, state, or local law. Our goal is for each staff member to have the opportunity to grow to the limits of their abilities and to achieve personal and organizational objectives. We will support positive programs for equal treatment of all staff and full utilization of all qualified employees at all levels within Navitas.
Shubham Vishwakarma
Full Stack Developer - Averlon
About TECHNAVITAS INFO INDIA PVT LTD
About
Company social profiles
Similar jobs (10)
The recruiter has not been active on this job recently. You may apply but please expect a delayed response.
Python
ScalaJob Description:
Position: Lead / Senior Data Engineer
Location: Chennai
Shift: US Eastern Time ( 5:00 PM – 2:00 AM )
Experience : 8+ years
Notice Period: Immediate Joiner only
Roles and Responsibilities:
Role Overview
The Lead Data Engineer will be responsible for designing, developing, and delivering high-quality software and data solutions while leading a team of engineers. The role involves hands-on technical work, architectural decision-making, mentoring junior developers, and collaborating with cross-functional teams to ensure successful delivery of scalable data platforms and analytical solutions.
Key Responsibilities:
Lead the end-to-end design, development, and delivery of software systems, data pipelines, and components.
Define technical strategy, architecture, and best practices for development and data engineering.
Design and build optimized data pipelines using cutting-edge technologies in a cloud environment.
Construct infrastructure for efficient ETL processes from various sources and storage systems.
Architect, design, and maintain database pipeline architectures, ensuring readiness for AI/ML transformations.
Lead the implementation of algorithms and prototypes to transform raw data into useful information.
Review code for quality, scalability, and performance.
Collaborate with Product Managers, Business Managers, Designers, and QA teams to translate business requirements into technical solutions.
Develop analytical tools, programs, and reporting mechanisms.
Create data validation methods and data analysis tools.
Interpret data trends and patterns to establish operational alerts.
Conduct complex data analysis and present results effectively.
Prepare data for prescriptive and predictive modeling.
Ensure compliance with data governance and security policies.
Troubleshoot, debug, and resolve complex technical issues.
Drive continuous improvement in software and data development processes, tools, and methodologies.
Mentor and guide engineers through code reviews, technical discussions, and training.
Ensure timely delivery of projects while maintaining high engineering standards.
Continuously explore opportunities to enhance data quality and reliability.
Apply strong programming and problem-solving skills to develop scalable solutions.
Demonstrate passion for testing strategy, problem-solving, and continuous learning.
Willingness to acquire new skills and knowledge.
Possess a product/engineering mindset to drive impactful data solutions.
Experience working in distributed environments with global teams.
Stay current with emerging technologies and industry trends to propose innovative solutions.
Technical Skills and Experience Requirements
Minimum 8+ years of hands-on experience designing, building, deploying, testing, maintaining, monitoring, and owning scalable, resilient, and distributed data pipelines.
High proficiency in Python, Scala, and Spark for applied large-scale data processing.
Expertise with big data technologies, including Spark, Data Lake, Delta Lake, and Hive.
Solid understanding of batch and streaming data processing techniques.
Proficient knowledge of the Data Lifecycle Management process, including data collection, access, use, storage, transfer, and deletion.
Expert-level ability to write complex, optimized SQL queries across extensive data volumes.
Experience with RDBMS and OLAP databases such as MySQL and Snowflake.
Familiarity with Agile methodologies.
Obsession for service observability, instrumentation, monitoring, and alerting.
Knowledge or experience in architectural best practices for building data lakes.
Qualifications - Bachelor’s degree in computer science, Engineering, Information Systems, or related field.
PythonAbout the Role
We are looking for a Senior Data Engineer with strong hands-on expertise in Databricks, Python, PySpark, and SQL to build scalable, high-performance data engineering solutions. You’ll architect and develop large scale, high-performance data pipelines capable of handling massive real-time and batch data volumes across multiple business systems. Databricks is the core enterprise data and processing platform for this role. You will also use Apache Airflow for workflow orchestration and dbt for ELT transformations, and will contribute to designing reliable, secure, and governed data platforms that enable analytics, reporting, and AI-driven use cases.
Key Responsibilities
- Design and implement large-scale data pipelines using Python/PySpark, Databricks, and Microsoft Fabric.
- Develop and optimize data processing workloads in Databricks using PySpark and Spark SQL, with a strong focus on scalability, reliability, performance, and maintainability.
- Develop and maintain dbt models including layered architecture, incremental models, snapshots, macros, testing, and documentation.
- Design, develop, and maintain Apache Airflow DAGs for orchestrating reliable, scalable, and observable data pipelines.
- Design and implement data quality, observability, and governance frameworks, including automated testing, monitoring, lineage, access control, and data privacy standards.
- Partner with analytics, product, and business stakeholders to turn requirements into trustworthy datasets, and raise the engineering bar through design discussions, code reviews, and mentoring junior engineers.
Required Skills
- Strong expertise in Python for developing scalable, modular, and production-ready data engineering applications.
- Strong expertise in PySpark, including DataFrame API, Spark SQL, Structured Streaming, partitioning strategies, joins, caching, handling data skew, and Spark performance optimization.
- Strong hands-on experience with Databricks for data ingestion, transformation, processing, and optimization, including Delta Lake, Unity Catalog, Databricks Workflows, notebooks, jobs, and Databricks-native data engineering capabilities.
- Strong experience in Databricks/Spark performance tuning, including query and job optimization, partitioning, file sizing, caching, join optimization, handling data skew, and efficient use of compute resources.
- Hands-on experience with Delta Lake, including transactional data processing, schema management, incremental data processing, and reliable batch and streaming data pipelines.
- Hands-on experience in developing dbt projects using layered architecture, incremental models, snapshots, macros/Jinja, testing, documentation, and deployment best practices.
- Expertise in advanced SQL and data modelling — dimensional modeling, slowly changing dimensions, schema evolution, and query optimization.
- Hands-on experience in developing and managing Apache Airflow DAGs, scheduling workflows, dependency management, retries, backfills, and operational monitoring.
- Hands-on experience with at least one major cloud platform (AWS, Azure or GCP).
- Strong problem-solving skills and the ability to work independently with business and analytics stakeholders.
Nice to Have
- Hands-on exposure to Microsoft Fabric for data integration and analytics.
- Experience using AI coding assistants (e.g. Claude Code, GitHub Copilot) as part of a development workflow.
- Familiarity with modern DevOps practices, including CI/CD pipelines, Infrastructure as Code (IaC), and containerization (Docker/Kubernetes).
- Domain expertise in financial services.
PythonScalaDescription
We are looking for Senior Data Engineers to join our Data Platform team and build scalable, high-performance data platforms that power data processing, analytics, and downstream applications.
The ideal candidate will have strong experience in distributed data processing, ETL pipelines, and Big Data technologies, with hands-on expertise in Apache Spark and Python Scala.
You will be responsible for designing, developing, and optimizing large-scale data pipelines while collaborating closely with cross-functional engineering teams to build reliable, production-grade data solutions.
Key Responsibilities
- Design, develop, and maintain scalable ETL and data processing pipelines for large-scale datasets.
- Build and optimize distributed data applications using Apache Spark and Python Scala.
- Develop reliable, high-performance data pipelines for batch and streaming workloads.
- Design and manage data workflows using Apache Airflow.
- Build and operate data workloads on AWS, with strong usage of Amazon S3 for large-scale data storage.
- Work with large datasets to ensure data quality, consistency, reliability, and performance.
- Collaborate with engineering, product, analytics, and other platform teams to deliver robust data solutions.
- Optimize data workflows for scalability, reliability, performance, and cost efficiency.
- Troubleshoot production issues, identify bottlenecks, and continuously improve platform performance.
Requirements
Candidates who demonstrate:
- 5+ years of experience in Data Engineering, Big Data Engineering, or a similar role.
- Strong hands-on experience with Apache Spark and Scala.
- Experience designing, building, and maintaining large-scale ETL pipelines.
- Strong hands-on experience with AWS, particularly Amazon S3.
- Hands-on experience with Apache Airflow for workflow orchestration and scheduling.
- Strong SQL skills and a solid understanding of distributed data processing concepts.
- Experience working with batch and/or streaming data pipelines.
- Excellent debugging, problem-solving, and performance optimization skills.
- Strong communication and collaboration skills.
Good to Have
- Experience with Databricks and the broader Databricks data platform.
- Familiarity with streaming technologies such as Apache Kafka.
- Experience working on large-scale data platforms handling high-volume data workloads.
- Exposure to additional AWS data services and cloud-native data architectures.
Responsibilities and JD
Job Description: We are looking for a Senior Developer with strong expertise in PySpark, Databricks, and Snowflake to build scalable data engineering solutions and enterprise data platforms.
Key Responsibilities:
- Design, develop, and maintain ETL/ELT pipelines using PySpark, Databricks, and Snowflake.
- Develop batch and real-time data processing solutions for structured and semi-structured data.
- Build and optimize Databricks notebooks, workflows, and Delta Lake solutions.
- Design and implement Snowflake databases, schemas, views, stored procedures, tasks, and streams.
- Develop scalable data models, data marts, and data warehouse solutions.
- Optimize PySpark jobs, Databricks workloads, and Snowflake queries for performance and cost efficiency.
- Implement data quality, validation, governance, and security controls.
- Collaborate with business stakeholders, architects, and cross-functional teams to deliver data solutions.
- Manage source control and CI/CD deployments using Git and Azure DevOps.
- Troubleshoot production issues, perform root cause analysis, and ensure pipeline reliability.
- Mentor junior team members and participate in code reviews and technical design discussions.
Required Skills: PySpark, Databricks, Snowflake, Python, SQL.
Experience: 5+ years of Data Engineering experience with strong hands-on expertise in PySpark, Databricks, and Snowflake.
PythonJob Description: Python + AI
Company: Wissen Technology
Location: Bangalore, India
Experience: 5+Years
Employment Type: Full-Time
Role: Python + AI / Data Engineer
About the Role
Wissen Technology is looking for experienced Python + AI / Data Engineering professionals to join our technology team in Bangalore. The ideal candidate will have strong hands-on experience in Python, Artificial Intelligence, Generative AI, PySpark, Snowflake, and data pipeline development.
The candidate should be capable of designing and developing scalable data and AI solutions, building robust ETL/ELT pipelines, working with large datasets, and integrating AI/ML capabilities into enterprise applications.
Key Responsibilities
- Design, develop, and maintain scalable data pipelines using Python and PySpark.
- Develop robust ETL/ELT pipelines for processing large volumes of structured and unstructured data.
- Build and optimize data processing solutions using Apache Spark / PySpark.
- Develop data ingestion and transformation pipelines into Snowflake.
- Design and implement scalable Snowflake data models, tables, views, and SQL transformations.
- Work with batch and, where applicable, real-time data processing pipelines.
- Build and integrate AI and Generative AI solutions using Python.
- Develop LLM-based applications, RAG solutions, AI agents, and AI-powered services.
- Integrate AI models with enterprise data platforms and data pipelines.
- Develop REST APIs and microservices using FastAPI, Flask, or Django.
- Perform data cleansing, transformation, validation, and quality checks.
- Optimize PySpark jobs, SQL queries, Snowflake workloads, and data pipelines for performance and scalability.
- Implement data pipeline monitoring, logging, error handling, and alerting.
- Work with cloud platforms such as AWS, Azure, or GCP.
- Collaborate with Data Scientists, Data Engineers, Software Engineers, Architects, and business stakeholders.
- Participate in technical design, architecture, code reviews, and production support.
- Mentor junior engineers and contribute to engineering best practices.
Preferred Qualifications
- Bachelor's or master's degree in computer science, Engineering, Data Science, Artificial Intelligence, or a related field.
- Experience working on enterprise-scale AI and data engineering projects.
- Experience combining Python + PySpark + Snowflake + AI/GenAI in production environments.
- Experience with Databricks is an advantage.
- Experience with AI Agents / Agentic AI and tool/function calling.
- Knowledge of distributed systems and cloud-native architecture.
- Experience leading technical initiatives or mentoring engineering teams.
The recruiter has not been active on this job recently. You may apply but please expect a delayed response.
PythonJob Title : Data Engineer – Databricks
Experience : 6+ Years
Location : Noida / Hyderabad / Chennai / Pune / Bengaluru (Hybrid)
Shift : IST (Normal Shift)
Job Summary :
We are seeking an experienced Data Engineer with strong expertise in Databricks, Snowflake, Python, and Spark to build and optimize scalable data pipelines and support AI/ML model deployments. The ideal candidate should have experience working with cloud-based data platforms and preferably possess exposure to the Healthcare domain.
Required Skills :
- Databricks (Preferred)
- Snowflake
- Python
- Apache Spark
- SQL
- Azure Cloud
- Kubernetes
- Apache Airflow
- GitHub & CI/CD Pipelines
- AI/ML Model Deployment
- Data Analytics
Preferred :
- Experience in the Healthcare domain.
- Strong understanding of scalable data engineering architectures and best practices.
Python
Amazon Web Services (AWS)About the Role
You'll be at the forefront of designing and implementing robust data platform solutions that power advanced analytics, AI, and machine learning. Working with modern cloud technologies, you'll build scalable data foundations that enable clients to make smarter, data-driven decisions.
Key Responsibilities
- Build scalable data pipelines using Snowflake, AWS, GCP, and Databricks.
- Design and optimize data models for AI and machine learning workloads.
- Develop reliable data foundations for MLOps, governance, and data lineage.
- Integrate data from multiple sources into modern data platforms.
- Leverage Snowpark ML and Snowflake's native AI capabilities.
- Ensure data platforms are secure, scalable, and high-performing.
What We're Looking For
- 5+ years of hands-on experience with Snowflake.
- Strong proficiency in SQL and Python.
- Experience with AWS, Azure, or GCP.
- Knowledge of cloud storage services such as S3, ADLS, or GCS.
- Strong understanding of Dimensional Modeling and Data Vault.
- Experience with Scala or Java is a plus.
Tech Stack
- Data Warehouse: Snowflake
- Programming: SQL, Python, Scala (Good to Have), Java (Good to Have)
- Cloud: AWS, Azure, GCP
- Storage: S3, ADLS, GCS
- AI/ML: Snowpark ML, MLOps
Perks & Benefits
- Public Speaking & Communication Program
- Mentoring Program with Senior Support Leads
- 360° Progress Reviews
- Weekly Learning Sessions & Guilds
- Paid Certifications
- Hackathons & Innovation Days
- Recognition & Rewards Programs
- Team Socials & Annual Offsites
- Employee Assistance Program (24/7 Wellbeing Support)
The Data People Shaping Tomorrow
Our client helps organizations unlock the power of data through modern cloud, analytics, and AI solutions. We believe in creating an environment where talented technologists can learn, innovate, and make a real impact while building cutting-edge data platforms for global clients. If you're passionate about data engineering and want to work with the latest technologies in AI, cloud, and analytics, we'd love to hear from you.
PythonJob Title : Senior Data Engineer – Databricks
Experience : 14 to 20 Years
Location : HSR Layout, Bangalore
Work Mode : Hybrid – 3 Days WFO
Shift : 11:30 AM – 07:30 PM IST
Positions : 2
Notice Period : Immediate Joiners Only
Interview : 1 Technical Round + 2 Client Rounds
Role Overview :
We are looking for a Senior Data Engineer to build and lead enterprise-scale data platforms for a Switzerland-based commodity client.
The role requires a strong hands-on Data Engineering professional with expertise in Databricks, PySpark, Python, SQL, and AWS, along with technical leadership and stakeholder management experience.
Must-Have Skills :
- 14 to 20 years of Data Engineering experience
- Databricks & Apache Spark / PySpark
- Python & SQL
- AWS Cloud
- Lakehouse Architecture
- ETL / ELT & Distributed Data Processing
- Batch & Streaming Pipelines
- Data Pipeline Optimization & Data Modeling
- CDC & Incremental Processing
- Git, CI/CD & Testing
- Data Quality, Monitoring & Observability
- Technical Leadership & Stakeholder Management
Key Responsibilities :
- Design and build scalable data pipelines using Databricks, PySpark, Python, SQL, and AWS.
- Own data products from design through production.
- Develop batch / streaming pipelines and reusable ETL / ELT frameworks.
- Optimize pipelines for performance, scalability, reliability, and cost.
- Design scalable data architectures and data models.
- Implement data quality, monitoring, lineage, and CI/CD practices.
- Lead technical discussions and mentor engineering teams.
- Collaborate with business stakeholders, architects, product owners, and engineering teams.
- Remain hands-on while providing technical leadership.
Ideal Candidate :
A 14 to 20 years experienced, hands-on Data Engineering leader with strong Databricks + PySpark + AWS expertise, excellent communication, stakeholder management, and experience delivering enterprise-scale data platforms.
🔴 Super Urgent : Only Bangalore-based immediate joiners.
KubernetesScalaDescription
We are looking for Senior Data Engineers to join our AdTech team and build scalable, high-performance data platforms that power advertising insights and analytics. The ideal candidate will have strong experience in distributed data processing, ETL pipelines, and Big Data technologies, with hands-on expertise in Spark and Scala.
You will work on designing, developing, and optimizing large-scale data pipelines while collaborating closely with cross-functional engineering teams to build reliable, production-grade data solutions.
Key Responsibilities
- Design, develop, and maintain scalable ETL pipelines for large-scale data processing.
- Build and optimize distributed data applications using Spark and Scala.
- Develop reliable, high-performance data pipelines for batch and streaming workloads.
- Work with large datasets to ensure data quality, consistency, and performance.
- Collaborate with engineering, product, and analytics teams to deliver robust data solutions.
- Optimize data workflows for scalability, reliability, and cost efficiency.
- Deploy and manage data workloads in cloud and containerized environments.
- Troubleshoot production issues and continuously improve platform performance.
Requirements
Candidates who demonstrate:
- 5+ years of experience in Data Engineering or Big Data Engineering.
- Strong hands-on experience with Apache Spark and Scala.
- Experience building and maintaining ETL pipelines.
- Familiarity with Google Cloud Storage (GCS).
- Experience with Kubernetes (K8s).
- Strong SQL skills and understanding of distributed data processing.
- Excellent debugging, problem-solving, and performance optimization skills.
- Strong communication and collaboration skills.
Good to Have
- Experience with AWS and cloud-native data services.
- Familiarity with streaming technologies such as Kafka.
- Experience working on large-scale data platforms or AdTech systems.
- Exposure to orchestration tools such as Airflow.
Benefits
- Best-in-class salary: We hire strong talent and compensate accordingly.
- Proximity Talks: Meet and learn from designers, engineers, product leaders, and AI practitioners.
- Continuous learning: Work with a world-class team and stay close to the latest in AI, engineering, and product development.
- High-impact work: Build AI-first systems and products used at scale by global clients.
About Us
Proximity is the trusted technology, design, and consulting partner for some of the biggest Sports, Media, and Entertainment companies in the world. We’re headquartered in San Francisco and have offices in Palo Alto, Dubai, Mumbai, and Bangalore.
Since 2019, Proximity has built high-impact, scalable products used by millions of users every day. Today, we are a global team of engineers, designers, product managers, and experts solving complex problems and building cutting-edge technology at scale.
Senior Data Engineer – Ab Initio | GCP | Spark | Agentic AI
Location: Bangalore
Experience: 5+ Years
Role: Senior Data Engineer
Work Mode: Bangalore
Job Summary
We are looking for an experienced Senior Data Engineer with strong expertise in Ab Initio, GCP, Apache Spark, and Agentic AI. The ideal candidate will have hands-on experience designing and developing scalable data engineering solutions, building data pipelines, and working with modern cloud and AI technologies.
The candidate should be comfortable working across traditional enterprise data platforms and emerging Generative AI / Agentic AI solutions.
Key Responsibilities
- Design, develop, and maintain scalable and high-performance data pipelines using Ab Initio, Spark, and GCP services.
- Develop and optimize complex ETL/ELT workflows using Ab Initio.
- Build and maintain data processing solutions using Apache Spark / PySpark.
- Develop cloud-based data solutions on Google Cloud Platform (GCP).
- Work with GCP data services such as BigQuery, Cloud Storage, Dataflow, Dataproc, Pub/Sub, or equivalent services.
- Perform data integration, transformation, cleansing, and validation.
- Optimize data pipelines for performance, scalability, reliability, and cost.
- Collaborate with data architects,
Follow CutshortShubham Vishwakarma
Full Stack Developer - Averlon