Data Engineer – AWS & Databricks
We are looking for a Data Engineer to design, build, and enhance scalable data pipelines and Lakehouse platforms using AWS and Databricks for a major organization in the banking/financial services sector.
This role offers the chance to work on large-scale, enterprise data platforms with strict standards for security, governance, and performance.
Key Responsibilities
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Design, implement, and support ETL/ELT pipelines on Databricks using PySpark, Spark SQL, and Delta Lake
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Develop robust data ingestion solutions leveraging AWS services, including:
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S3, Glue, Lambda, and Step Functions
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Streaming platforms such as Kafka/MSK or Kinesis
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Connectivity with on-premise systems, RDS, and Redshift
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Orchestrate and automate data workflows using Databricks Workflows, Airflow, or equivalent tools
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Improve Lakehouse efficiency through partitioning strategies, Delta optimization, and compute/cost tuning
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Build and maintain data quality controls, monitoring, and production issue resolution processes
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Enforce data governance and security standards, including PII handling, access management, and audit compliance
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Work closely with data analysts, data scientists, and business teams to deliver reliable, business-ready datasets
Requirements
Must-have
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2–5+ years of hands-on experience in Data Engineering
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Strong practical experience with:
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AWS (S3, Glue, Lambda, IAM, Step Functions)
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Databricks (PySpark, Delta Lake, Workflows)
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Python and SQL
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Solid understanding of data modeling concepts (relational and dimensional)
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Experience working with large-scale, distributed data platforms
Nice-to-have
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Background in banking or financial services (e.g., core banking, payments, lending, regulatory reporting)
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Experience with real-time/streaming data processing (Kafka/MSK, Kinesis, Spark Structured Streaming)
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Familiarity with data governance and catalog solutions (Unity Catalog is a plus)
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Infrastructure-as-Code experience (Terraform or AWS CDK)
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Understanding of security and compliance requirements in regulated environments