Senior Data Engineer with AWS

Job Title : AWS Data Engineer / Data Lake Engineer

Experience : 2 to 4 Years

Location : Gurugram Sector 43

Work Mode : 5 Days WFO

Employment Type : Full-Time

About the Role :

We are looking for a skilled AWS Data Engineer with 2 to 4 years of experience in designing, developing, and maintaining data pipelines and cloud-based data platforms.

The candidate will work on an enterprise AWS Data Lake platform that collects data from multiple business systems, processes large datasets, and makes them available for analytics, reporting, and application development.

The ideal candidate should have strong hands-on experience with AWS data services, Python, PySpark, SQL, and Apache Airflow, along with an understanding of data warehousing and API-based data serving.

Mandatory skills :

AWS Data Lake, S3, EMR, Glue, Athena, Redshift, Lambda, RDS, DMS, IAM, Python, PySpark, Apache Airflow, Advanced SQL, ETL / ELT Pipelines, Data Modeling, PostgreSQL, Production Support.

Key Responsibilities :

• Develop, maintain, and optimize data ingestion and ETL / ELT pipelines on AWS.
• Work with AWS services such as S3, Glue, EMR, Athena, Redshift, Lambda, RDS, and DMS.
• Build and manage data processing workflows using Python, PySpark, and Apache Airflow.
• Implement data lake architecture with staging, curated, and consumption layers.
• Write complex SQL queries and perform data transformation, validation, and modeling.
• Work with PostgreSQL and RDS for structured data storage and consumption.
• Support API-based data access using Hasura GraphQL or similar technologies.
• Monitor production data pipelines, troubleshoot failures, and perform root cause analysis.
• Optimize Spark jobs, SQL queries, and data pipelines for performance and cost efficiency.
• Follow AWS security practices, IAM permissions, and data governance standards.
• Collaborate with application, analytics, and business teams to understand data requirements.

Mandatory Skills :

• 2 to 4 years of experience in Data Engineering, AWS Data Engineering, or Data Platform Development.
• Strong hands-on experience with AWS services : S3, Glue, EMR, Athena, Redshift, Lambda, RDS, DMS, and IAM.
• Good experience in Python and PySpark.
• Practical experience with Apache Airflow for workflow orchestration.
• Strong SQL skills and understanding of database concepts.
• Experience in building and supporting ETL / ELT pipelines.
• Understanding of Data Lake architecture, data warehousing, and data modeling.
• Working knowledge of PostgreSQL or similar relational databases.
• Experience handling production pipeline issues and troubleshooting.

Good to Have :

• Experience with Hasura GraphQL or any GraphQL-based API / data-serving platform.
• Knowledge of DBT (Data Build Tool).
• Understanding of Lakehouse architecture and table formats such as Apache Iceberg or Delta Lake.
• Experience with Kafka or other streaming data technologies.
• Knowledge of Amazon Redshift performance optimization, distribution keys, and sort keys.
• Experience with AWS Aurora and API Gateway.
• BFSI, Banking, or Insurance domain experience.

Ideal Candidate Profile :

• Strong hands-on technical and problem-solving skills.
• Experience working on real-time or batch-based production data pipelines.
• Good understanding of data processing, transformation, and storage concepts.
• Ability to troubleshoot technical issues and work independently.
• Good communication and collaboration skills.

Note : Candidates with strong practical experience in AWS Data Lake, PySpark, Airflow, Python, and SQL will be preferred. Real-world project experience is more important than theoretical knowledge or certifications.

🚀 Job Title : Data Engineer – AWS, PySpark & Airflow

Experience : 5+ Years

Industry : Health Insurance / BFSI

Location : Sector 43, Gurugram, Haryana

Work Mode : Work From Office (5 Days a Week)

Employment Type : Full-Time

Role Overview :

We are looking for an experienced Data Engineer with strong hands-on expertise in AWS Data Engineering, Data Lake Architecture, Python, SQL, PySpark, and Apache Airflow.

The candidate will be responsible for designing, developing, and maintaining scalable, reliable, and high-performance data pipelines using AWS cloud technologies. The role involves building end-to-end ETL / ELT workflows, implementing data transformation and processing solutions, optimizing pipeline performance, and ensuring the reliability of production data systems.

Mandatory Skills :

AWS Data Engineering | Data Lake Architecture | Amazon S3 | AWS Glue | Amazon EMR | Athena | Redshift | Python | SQL | PySpark | Apache Spark | Apache Airflow | ETL/ELT | PostgreSQL | Data Modelling | Pipeline Optimization | Production Support | Root Cause Analysis

Key Responsibilities :

• Design, develop, and maintain end-to-end data pipelines for data ingestion, staging, transformation, curation, and consumption.
• Build and manage AWS-based data lake solutions using Amazon S3, AWS Glue, Amazon EMR, Athena, and Redshift.
• Develop, maintain, and optimize ETL / ELT pipelines using Python, SQL, Apache Spark, and PySpark.
• Implement and manage workflow orchestration using Apache Airflow, including scheduling, dependency management, retries, monitoring, and failure handling.
• Integrate data from multiple structured and semi-structured sources.
• Design and maintain data models, dimensional models, and analytical data marts.
• Work with relational databases, including PostgreSQL and Amazon RDS.
• Optimize Spark jobs, SQL queries, data transformations, and cloud resource utilization.
• Implement data quality checks, validation rules, and error-handling mechanisms.
• Monitor production data pipelines, troubleshoot failures, and perform root cause analysis (RCA).
• Ensure pipeline reliability, scalability, performance, and compliance with service-level agreements (SLAs).
• Follow data security, access management, governance, and sensitive-data protection best practices.
• Collaborate with business, analytics, and application teams to deliver effective data solutions.
• Maintain technical documentation and follow established data engineering standards.

Required Technical Skills :

• AWS Data Engineering : Strong hands-on experience with AWS-based data engineering and data lake architecture.
• AWS Services : Amazon S3, AWS Glue, Glue Data Catalog, Amazon EMR, Athena, Redshift, AWS Lambda, and IAM.
• Programming : Strong proficiency in Python and SQL.
• Big Data Processing : Hands-on experience with Apache Spark and PySpark.
• Workflow Orchestration : Experience developing and managing workflows using Apache Airflow.
• ETL/ELT Development : Experience building scalable data ingestion, transformation, and processing pipelines.
• Database Technologies : Good understanding of relational databases and PostgreSQL concepts.
• Data Modelling : Experience with data modelling, data transformation, and analytical data structures.
• Production Support : Ability to monitor pipelines, troubleshoot failures, perform RCA, and resolve production issues.
• Performance Optimization : Experience optimizing SQL queries, Spark jobs, and data processing workflows.

Good-to-Have Skills :

• Experience with AWS Database Migration Service (DMS) and Amazon RDS.
• Familiarity with dbt for data transformation and modelling.
• Knowledge of Delta Lake, Apache Iceberg, or Apache Hudi.
• Exposure to Apache Kafka and real-time data streaming.
• Familiarity with GraphQL, Hasura, or similar data-serving technologies.
• Understanding of data governance and data quality frameworks.
• Experience in the BFSI, healthcare, or health insurance domain.

Candidate Requirements :

• 5+ years of relevant experience in Data Engineering.
• Proven hands-on experience building and maintaining production-grade data pipelines on AWS.
• Strong understanding of data lake architecture and large-scale data processing.
• Practical experience with Apache Airflow and Spark / PySpark-based transformations.
• Strong programming, analytical, debugging, and problem-solving skills.
• Experience handling production incidents, pipeline failures, and performance bottlenecks.
• Ability to take ownership of deliverables and work effectively with cross-functional teams.
• Good communication, collaboration, and technical documentation skills.
• Willingness to work from the Gurugram office five days a week.

Ideal Candidate :

The ideal candidate is a hands-on Data Engineer who can independently build, optimize, and maintain AWS-based data pipelines, work with large datasets using PySpark, orchestrate workflows through Airflow, and resolve production issues in a business-critical environment.

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

Job Title : Senior AWS Data Engineer – Data Lake Platform

Experience : 5+ Years

Location: Gurugram Sector 43, Haryana

Work Mode : Work From Office (5 Days a Week – Monday to Friday)

Open Positions : 8+

About the Role :

We are looking for a skilled Senior AWS Data Engineer with strong hands-on experience in building, managing, and optimizing enterprise-scale data lake platforms on AWS.

The ideal candidate will have expertise in AWS data services, PySpark, Apache Airflow, and large-scale data pipeline development. The role involves designing end-to-end data ingestion and processing workflows, maintaining data quality, optimizing platform performance, and supporting analytics and downstream data consumption requirements.

Mandatory skills :

AWS Data Lake, Amazon S3, EMR, Glue, Athena, Redshift, DMS, Lambda, RDS, IAM, PySpark, Apache Spark, Apache Airflow, Python, Advanced SQL, Data Modeling, Production Data Pipelines, Hasura/GraphQL, PostgreSQL, and Production Support.

Key Responsibilities :

• Design, develop, and maintain scalable AWS-based data lake platforms and end-to-end data pipelines.
• Build data ingestion, staging, transformation, curated, and consumption layers using AWS services.
• Develop and optimize data processing pipelines using PySpark, Apache Spark, and AWS EMR.
• Implement workflow orchestration and scheduling using Apache Airflow.
• Work with Amazon S3, AWS Glue, Glue Data Catalog, Amazon Athena, and Amazon Redshift for data storage, cataloging, processing, and analytics.
• Develop efficient SQL queries and implement data modeling strategies for enterprise reporting and analytics.
• Build and support data-serving solutions using Amazon RDS, AWS Lambda, API Gateway, and PostgreSQL-based platforms.
• Monitor pipeline performance, troubleshoot production issues, perform root-cause analysis, and ensure data pipeline SLAs.
• Implement data security, access controls, PII protection, and governance best practices across the platform.
• Optimize data processing performance, query execution, storage utilization, and AWS infrastructure costs.
• Collaborate with application, analytics, and business teams to deliver reliable and scalable data solutions.

Mandatory Skills :

• AWS Data Services : Amazon S3, EMR, Glue, Athena, Redshift, DMS, Lambda, RDS, and IAM.
• Data Engineering : Strong hands-on experience building and maintaining enterprise data lake platforms.
• Programming : Python, PySpark, and Apache Spark.
• Orchestration : Production experience with Apache Airflow.
• Database & SQL : Advanced SQL, data modeling, and query optimization.
• Data Architecture : Experience with data ingestion, staging, transformation, curated layers, and consumption architecture.
• Production Support : Pipeline monitoring, troubleshooting, root-cause analysis, and SLA management.
• API / Data Serving : Experience with Hasura or a similar GraphQL-based data-serving layer integrated with PostgreSQL.
• Platform Ownership : Ability to independently manage data pipelines, improve platform reliability, and collaborate with cross-functional teams.

Good-to-Have Skills :

• Experience with dbt for data transformation and modeling.
• Knowledge of data lakehouse architectures and table formats such as Apache Iceberg, Delta Lake, or Apache Hudi.
• Experience with Amazon Redshift workload optimization, distribution keys, sort keys, and Spectrum.
• Knowledge of Kafka and real-time or streaming data ingestion.
• Experience with PostgreSQL or Amazon Aurora as a data-serving layer.
• Familiarity with data governance, compliance, and sensitive-data management.
• Experience in the BFSI or health insurance domain.

Ideal Candidate Profile :

• 5+ years of hands-on experience in AWS Data Engineering.
• Strong experience building and supporting production-grade AWS data lake platforms.
• Practical expertise in PySpark, Apache Airflow, SQL, and AWS data services.
• Ability to troubleshoot complex data pipeline issues and optimize performance.
• Strong understanding of data architecture, data modeling, and production support practices.
• Candidates with experience in enterprise data platforms and insurance or BFSI environments will be preferred.

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

Job Summary

We are looking for an experienced Data Engineer to build and optimize scalable data pipelines and AWS-based data platforms.

Key Responsibilities

• Develop and maintain data pipelines using Python and PySpark.
• Design and implement data lakehouse solutions on AWS.
• Build and manage data workflows using AWS Glue, EMR, ECS, and Step Functions.
• Work with DynamoDB and other data storage technologies.
• Monitor data pipelines and ensure data quality, performance, and reliability.
• Collaborate with stakeholders and engineering teams to deliver data solutions.

Mandatory Skills

• 5+ years of overall professional experience.
• 3+ years of experience in data platforms and big data technologies.
• Strong proficiency in Python and PySpark.
• Hands-on experience with AWS EMR, ECS, Glue, Step Functions, and DynamoDB.
• Experience designing and implementing AWS data lakehouse architectures.

Good to Have

• Apache Spark and Apache Iceberg.
• AWS Lambda, EventBridge, Kinesis, and Redshift.
• Experience with real-time data pipelines and event-driven architectures.

Job Summary

We are looking for a skilled and experienced Data Engineer to join our growing data team. The ideal candidate will have strong expertise in Python, PySpark, Data Modeling, and Power BI, with hands-on experience in designing, developing, and optimizing scalable data solutions. The role requires working closely with business stakeholders, data architects, and analytics teams to build robust data pipelines and semantic models that enable data-driven decision-making.

Technical Skills

• Strong hands-on experience in Python and PySpark development.
• Expertise in building and optimizing Data Engineering solutions and ETL pipelines.
• Strong understanding of Data Modeling concepts (Star Schema, Snowflake Schema, Dimensional Modeling).
• Experience with Power BI Data Modeling and Semantic Layer development.
• Proficiency in DAX (Data Analysis Expressions).
• Experience designing and managing Semantic Models in Power BI.
• Strong SQL skills and experience working with large datasets.
• Knowledge of data warehousing concepts and best practices.

Preferred Skills

• Experience with cloud platforms such as Azure, AWS, or GCP.
• Exposure to modern data platforms like Databricks.
• Understanding of data governance and data quality frameworks.

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.

Senior Data Engineer – PySpark & Oracle

Experience: 7+ Years

Location: Bangalore

Notice Period: Immediate to 10 Days

Key Skills:

• Strong expertise in Data Modeling, Data Design & Modernization
• Primary skills: PySpark, Oracle SQL/PLSQL
• Secondary skills: Python, ETL & Data Pipelines
• Experience with Kafka and Hadoop
• Exposure to AWS / Azure / GCP
• Good knowledge of Git and JIRA

Roles & Responsibilities:

• Design, develop, and modernize scalable data models and data architecture.
• Develop and optimize data processing solutions using PySpark and Oracle SQL/PLSQL.
• Build and maintain robust ETL workflows and data pipelines.
• Work with Kafka, Hadoop, and cloud platforms for data processing and integration.
• Perform data transformation, optimization, and performance tuning.
• Collaborate with technical teams on data design, development, testing, and deployment.