AWS data engineer

Job Title : Tech Lead – Data Lake Platform

Number of Positions : 2

Experience : 7+ Years

Role Type : Technical Lead / Data Platform Lead

Domain : Data Engineering / Data Platform / AWS

About the Role :

We are looking for an experienced Tech Lead – Data Lake Platform to lead the design, development, and operations of an enterprise-scale AWS-based Data Lake Platform.

The platform will ingest data from multiple business systems, process it through structured data layers, and serve data for analytics, reporting, APIs, and operational applications.

As a Tech Lead, you will be responsible for setting the technical direction, leading data engineering teams, driving platform reliability and performance, and owning the platform's delivery, governance, and production support end to end.

Core Tech Stack :

AWS | S3 | EMR | Glue | Athena | Redshift | DMS | Lambda | RDS | IAM | Airflow | Spark/PySpark | SQL | Data Modeling | Hasura | GraphQL | DBT | PostgreSQL/Aurora | Kafka

Key Responsibilities :

• Own the overall Data Lake Platform architecture, covering data ingestion, staging, curated layers, consumption, analytics, and API/data serving.
• Lead the design and development of production-grade data pipelines using AWS Glue, EMR/Spark, Airflow, DBT, Athena, and Redshift.
• Design scalable batch and analytics pipelines with a focus on reliability, performance, data quality, and maintainability.
• Own the API/data-serving architecture from consumption data → RDS/PostgreSQL → Hasura GraphQL → Lambda/API Gateway.
• Drive improvements in platform stability, including orchestration failures, cluster sizing, pipeline SLAs, query performance, and production reliability.
• Design and implement appropriate AWS security, access control, IAM, PII handling, and data governance practices.
• Lead technical discussions, architecture decisions, code reviews, and engineering best practices.
• Mentor and guide data engineers while ensuring high-quality and scalable engineering delivery.
• Own production support, incident management, troubleshooting, and root-cause analysis (RCA) for critical data platform issues.
• Develop and maintain runbooks, operational procedures, monitoring, and incident response practices.
• Collaborate with business, product, application, and analytics teams to onboard new datasets and support reporting, API, and data consumption requirements.
• Ensure the platform meets defined availability, performance, security, data quality, and compliance requirements.

Must-Have Skills :

• 7+ years of experience in Data Engineering, Data Platform Engineering, or related areas, including experience in technical leadership or architecture.
• Strong hands-on experience with AWS Data Services, including:
• Amazon S3
• AWS EMR
• AWS Glue
• Amazon Athena
• Amazon Redshift
• AWS DMS
• AWS Lambda
• Amazon RDS
• AWS IAM
• Strong production experience with Apache Airflow.
• Strong hands-on experience with Apache Spark / PySpark.
• Strong SQL skills and experience with data modeling, including layered data architecture, data marts, and enterprise data models.
• Experience with Hasura or a similar GraphQL layer for PostgreSQL-based data/API serving.
• Strong understanding of data lake architecture and enterprise data platforms.
• Proven experience leading engineers and driving technical decisions.
• Hands-on experience with production support, troubleshooting, incident management, and RCA.
• Strong understanding of data platform performance, scalability, reliability, and SLA management.

Good-to-Have Skills :

• Experience with DBT and modern data transformation practices.
• Experience with lakehouse table formats on Amazon S3, such as Apache Iceberg or similar technologies.
• Strong knowledge of Amazon Redshift workload optimization, including :
• Distribution keys
• Sort keys
• Spectrum
• External tables
• Experience with Kafka or other streaming/data ingestion technologies.
• Experience with PostgreSQL / Amazon Aurora as a data-serving layer.
• Experience with Lambda and API Gateway for API-based data serving.
• Experience with enterprise data governance, data quality, security, and compliance.
• Experience in BFSI / Banking / Financial Services / Insurance domain.

Ideal Candidate :

The ideal candidate is a hands-on Data Platform / Data Engineering Lead who can operate at both the architecture and implementation level. You should be comfortable designing an AWS Data Lake from end to end, leading engineers, troubleshooting production issues, and working closely with business and application teams to deliver reliable data products.

Job 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.

Strong Data Engineer (AWS) Profile

2

Mandatory (Experience 1) – Must have minimum 6+ years of experience in Data Engineering, with strong experience designing, developing, and maintaining scalable data pipelines and ETL processes.

3

Mandatory (Experience 2) – Must have 3+ years of hands-on experience with AWS data engineering services, particularly S3, AWS Glue, EMR, Lambda, and Redshift.

4

Mandatory (Experience 3) – Must have strong programming experience in Python, Java, or Scala, with hands-on development of data processing and pipeline solutions.

5

Mandatory (Experience 4) – Must have strong expertise in SQL, data warehousing, and database technologies, including experience with both SQL and NoSQL databases.

6

Mandatory (Experience 5) – Must have hands-on experience working with Big Data technologies and distributed data processing, including designing and optimizing large-scale data pipelines.

7

Mandatory (Experience 6) – Must have experience with data pipeline troubleshooting, monitoring, data quality, governance, and security best practices.

8

Mandatory (Education) – B.Tech / B.E./ M.Tech

Strong Data Engineer (AWS) Profile

2

Mandatory (Experience 1) – Must have minimum 6+ years of experience in Data Engineering, with strong experience designing, developing, and maintaining scalable data pipelines and ETL processes.

3

Mandatory (Experience 2) – Must have 3+ years of hands-on experience with AWS data engineering services, particularly S3, AWS Glue, EMR, Lambda, and Redshift.

4

Mandatory (Experience 3) – Must have strong programming experience in Python, Java, or Scala, with hands-on development of data processing and pipeline solutions.

5

Mandatory (Experience 4) – Must have strong expertise in SQL, data warehousing, and database technologies, including experience with both SQL and NoSQL databases.

6

Mandatory (Experience 5) – Must have hands-on experience working with Big Data technologies and distributed data processing, including designing and optimizing large-scale data pipelines.

7

Mandatory (Experience 6) – Must have experience with data pipeline troubleshooting, monitoring, data quality, governance, and security best practices.

8

Mandatory (Education) – B.Tech / B.E./ M.Tech

Key Responsibilities

• Design, develop, and maintain scalable data pipelines using Python, Hadoop, and Apache Spark.
• Develop efficient data processing and transformation workflows for large datasets.
• Write, optimize, and maintain complex SQL queries and PL/SQL procedures.
• Perform data extraction, transformation, and loading (ETL) from multiple data sources.
• Work with structured and unstructured data to support analytics and reporting requirements.
• Optimize data pipelines and queries for performance, scalability, and reliability.
• Ensure data quality, consistency, security, and integrity across data platforms.
• Collaborate with cross-functional teams to understand business requirements and deliver data engineering solutions.
• Use Git, automation, and CI/CD practices to support development and deployment.

Required Skills

• 4.5–10 years of experience in software development or data engineering.
• Strong proficiency in AWS
• Hands-on experience with Hadoop and Apache Spark.
• Good knowledge of SQL and PL/SQL, including query optimization and stored procedures.
• Experience in ETL development, data processing, and data pipeline design.
• Understanding of data warehousing concepts and distributed computing.
• Strong problem-solving, debugging, and performance-tuning skills.

Tech Lead – Data Lake Platform

Recruitment & Payroll Company: Neogencode Technologies

Job Location: Sector 43, Gurugram, Haryana

Work Mode: On-site (Work from Office)

Working Days: 5 days a week

Experience Required: 5+ years

Employment Type: Full-time

About the Opportunity

We are hiring a Tech Lead – Data Lake Platform. This opportunity involves working on an enterprise-grade AWS data lake platform supporting data engineering, analytics, reporting, and business applications.

The candidate will be employed on the payroll of Neogencode Technologies and will work at the client location in Sector 43, Gurugram.

Key Responsibilities

• Own and design end-to-end data lake architecture, including data ingestion, staging, curated layers, and data consumption.
• Lead data engineering pipelines using AWS services, Apache Airflow, Spark/PySpark, and dbt.
• Manage AWS data services such as S3, EMR, Glue, Athena, Redshift, Lambda, RDS, IAM, and DMS.
• Design and support API/data-serving solutions using PostgreSQL, Hasura GraphQL, or similar technologies.
• Improve pipeline performance, platform reliability, monitoring, and service-level agreements (SLAs).
• Ensure data security, access control, personally identifiable information (PII) protection, and governance.
• Mentor data engineers, conduct code reviews, and handle production incidents and root-cause analysis.
• Collaborate with business and application teams to deliver data and reporting solutions.

Required Skills

• 5+ years of experience in data engineering or platform engineering, including technical leadership.
• Strong hands-on experience with AWS data services and enterprise data lake architecture.
• Production experience with Apache Airflow, Spark/PySpark, and SQL.
• Strong understanding of data modeling and layered data architecture.
• Experience with PostgreSQL and Hasura GraphQL or a similar API/data-serving layer.
• Proven experience in technical leadership, production support, and troubleshooting.

Good to Have

• Experience with dbt and lakehouse table formats.
• Knowledge of Kafka or streaming data ingestion.
• Experience with Redshift optimization and workload design.

Job Overview:

We are seeking an experienced Cloud Data Engineer to the architecture, development, Data migration, ETL pipeline in cloud and optimization of a next-generation data platform. This is a development role for an individual with over 4 years of deep data engineering and data migration, data pipeline in AWS expertise, capable of driving technical direction, and delivering high-impact solutions in a fast-paced

4+ Years of hands-on, progressive experience in Cloud Data Engineering, cloud ecosystems AWS, Data Architecture

• Deep conceptual understanding of core data engineering principles, ETL/ELT patterns, and metadata management

•  Ability to translate complex business needs into actionable technical requirements.

•  Strong troubleshooting and problem-solving skills in distributed systems.

•  Agile/Scrum teamwork capabilities and version control practices

• Insurance industry experience preferred but not mandatory

• Tools:

o Cloud Environment: AWS (S3, IAM, VPC, Glue, Lambda etc.)

o Experience with dbt Core, Iceberg, AWS Glue Catalog, AWS lambda, Redshift, Athena, Splunk, AmazonQ, AWS streaming services, Git

o Strong SQL, Pyspark and Python

Key Skills:

Python & System Design: Strong expertise in Python and system design, with proven experience architecting scalable, production-grade systems.

LLM & Agentic AI: Proven experience building LLM-based and agentic systems from the ground up and evaluating them based on reproducibility, calibration, and cost.

Cloud & Data Architecture: Deep understanding of data and cloud architecture across AWS and Azure, including multi-store data design, distributed systems, streaming, security, and cost optimization.

Azure Databricks: Hands-on production experience with Azure Databricks architecture, including Unity Catalog, Delta Lake, cluster policy design, job orchestration, and Terraform provisioning.

Supply Chain & Inventory Management: Strong domain knowledge of supply chain and inventory operations, including demand planning, MRP logic, inventory parameters, and translating business planning decisions into data requirements.

System Architecture & Technical Decision-Making: Ability to make sound architectural decisions based on first principles, evaluate technical trade-offs, and avoid unnecessary complexity and abstraction.

LLM & Agentic AI: Proven experience building LLM-based and agentic systems from the ground up and evaluating them based on reproducibility, calibration, and cost.

Technical Leadership: Ability to influence teams without direct authority, establish technical standards, prepare technical specifications and documentation, and mentor team members.

Experience in pharmaceutical or regulated industries is highly valued.

Description

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.

Job Title : AWS Data Engineer

Experience : 4+ Years

Location : Bengaluru (HSR – Hybrid, 3 Days WFO)

Notice Period : Immediate Joiner

💡 Role Overview :

We are looking for a skilled AWS Data Engineer to design, build, and scale modern data platforms. The role involves working with AWS-native services, Python, Spark, and DBT to deliver secure, scalable, and high-performance data solutions in an Agile environment.

🔥 Mandatory Skills :

Python, SQL, Spark, AWS (S3, Glue, EMR, Redshift, Athena, Lambda), DBT, ETL/ELT pipeline development, Airflow/Step Functions, Data Lake (Parquet/ORC/Iceberg), Terraform & CI/CD, Data Governance & Security

🚀 Key Responsibilities :

• Design, build, and optimize ETL/ELT pipelines using Python, DBT, and AWS services
• Develop and manage scalable data lakes on S3 using formats like Parquet, ORC, and Iceberg
• Build end-to-end data solutions using Glue, EMR, Lambda, Redshift, and Athena
• Implement data governance, security, and metadata management using Glue Data Catalog, Lake Formation, IAM, and KMS
• Orchestrate workflows using Airflow, Step Functions, or AWS-native tools
• Ensure reliability and automation via CloudWatch, CloudTrail, CodePipeline, and Terraform
• Collaborate with data analysts and data scientists to deliver actionable insights
• Work in an Agile environment to deliver high-quality data solutions

✅ Mandatory Skills :

• Strong Python (including AWS SDKs), SQL, Spark
• Hands-on experience with AWS data stack (S3, Glue, EMR, Redshift, Athena, Lambda)
• Experience with DBT and ETL/ELT pipeline development
• Workflow orchestration using Airflow / Step Functions
• Knowledge of data lake formats (Parquet, ORC, Iceberg)
• Exposure to DevOps practices (Terraform, CI/CD)
• Strong understanding of data governance and security best practices
• Minimum 4–7 years in Data Engineering (3+ years on AWS)

➕ Good to Have :

• Understanding of Data Mesh architecture
• Experience with platforms like Data.World
• Exposure to Hadoop / HDFS ecosystems

🤝 What We’re Looking For :

• Strong problem-solving and analytical skills
• Ability to work in a collaborative, cross-functional environment
• Good communication and stakeholder management skills
• Self-driven and adaptable to fast-paced environments

📝 Interview Process :

1. Online Assessment
2. Technical Interview
3. Fitment Round
4. Client Round