AWS Data Engineer / Data Lake Engineer
First seen by Alion on Nov 6, 2026.
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.
Skills
Amazon Web Services (AWS), AWS Data Lake, Amazon S3, Amazon EMR, AWS Glue, AWS Athena, Amazon Redshift, AWS Lambda, AWS RDS, AWS DMS, AWS IAM, Python, PySpark, Apache Airflow, Advanced SQL, ETL, ELT, Data modeling, PostgreSQL, hasura, GraphQL