Data Engineer – AWS (Engineer / Senior / Lead)
Impelsys (company site, opens in a new tab)10+ yrsBengaluruOn-siteDirect career pageEst. 28–42 LPA
Posted 16h ago
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Job description
Structured overview of role & requirements
About This Role
- Develop, test, and operate end-to-end data ingestion, transformation, and data-quality pipelines on AWS cloud environment.
- Manage and model data within layered lakehouse/warehouse architectures and migrate legacy SQL workflows to modern cloud-native data pipelines using AI-assisted tools.
- For Senior and Lead levels: set coding standards, plan work, conduct code reviews, mentor engineers, and optimize performance and cost for data platforms.
Minimum Requirements
- Experience required varies by level: Data Engineer (4–7 yrs total, 3+ yrs Spark/SQL pipeline, 2+ yrs AWS), Senior (7–10 yrs total, 5+ yrs data engineering, 3+ yrs AWS), Lead (10+ yrs total, 6+ yrs data engineering, 4+ yrs AWS, 2+ yrs team leadership).
- Mandatory skills include Python, PySpark, advanced SQL, dbt, Apache Airflow, AWS services (S3, Glue, Athena, EMR; Redshift preferred), data modelling with upsert/deduplication patterns, Git and CI/CD practices, and data-quality testing.
- Location: Bangalore (Offshore India); full-time role.
- Work Experience Required: as specified above per level. Notice period: Not explicitly mentioned in the JD.
Ideal Candidate Profile
- Experienced in building modern cloud data platforms and migrating legacy data workloads to AWS with a strong focus on Spark, SQL, and data platform orchestration tools.
- Capable of leading or mentoring teams (Senior/Lead) with ability to enforce coding standards and architectural best practices in a collaborative remote environment.
- Comfortable engaging with global remote client stakeholders and working in agile settings using AI-assisted engineering tools to improve productivity and code quality.