AWS Data Engineer
EXL (company site, opens in a new tab)PuneOn-siteDirect career pageEst. 12–20 LPA
Posted on 8 Sept
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Job description
Structured overview of role & requirements
About This Role
- Design and implement scalable, secure, and cost-optimized AWS data architectures integrating multiple internal and external financial data sources.
- Develop and maintain ETL pipelines, building and optimizing applications using Apache Spark (PySpark) and Apache Iceberg on AWS data lakes.
- Implement event-driven workflows and federated querying solutions using AWS Step Functions, EventBridge, Starburst, and automate deployments with CI/CD pipelines.
Minimum Requirements
- Strong hands-on experience with AWS services: Lambda, Glue ETL, Athena, S3, DynamoDB, Step Functions, EventBridge, SNS, SQS.
- Proficiency in Apache Spark development using PySpark, including unit testing and performance tuning.
- Experience working with Apache Iceberg for data lake design and optimization.
- Work Experience Required: Not explicitly mentioned in the JD
Ideal Candidate Profile
- Experienced in designing and operating enterprise-scale AWS data architectures and event-driven data workflows in fast-paced, high-visibility Finance programs.
- Skilled in integrating modern big data technologies including Spark, Iceberg, Starburst, and streaming platforms like Apache Kafka.
- Comfortable with building generic, reusable data engineering solutions and optimizing performance in complex data lake environments.