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AWS Data Engineer Lead

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Onsite

AWS Data Engineer Lead

Valuemomentum

AWS Data Engineer Lead

Skills

SQL QueriesDynamoDBPythonPostgreSQLAWS LambdaScalaAWSPySparkTerraform

1 month ago

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Important information


Contract type:

Permanent

Location:

Coimbatore, Tamil Nadu, India

Work mode:

Onsite

Published on:

24 September 2026

What they need


Job Title:AWS Data Engineer Lead

Primary Skill: AWS, DynamoDB, Aurora PostgreSQL, AWS Sagemaker, Spark, Scala/Python,Medallion Architecture

Location: Hyderabad/ Pune/ Coimbatore

Experience: 7-10 years of experience

Mode of working - 4 Days work from office

We are looking for an experienced AWS Data Engineer with strong handson expertise in building scalable, reliable data pipelines in a Lakehouse/Medallion architecture. The ideal candidate will have deep knowledge of Spark/PySpark, Terraform, and modern data engineering patterns.

Key Responsibilities

  • Build and operate data pipelines using PySpark/Spark, SQL, and AWS services.
  • Implement Medallion (Bronze/Silver/Gold) architecture with proper separation of concerns.
  • Design incremental processing, SCD Type 2, CDC/merge, and late-arriving/outofsequence data handling.
  • Ensure pipelines are idempotent, resilient, and reprocessable.
  • Manage AWS data infrastructure using Terraform.
  • Apply strong data quality, deduplication, schema validation, and operational best practices.

Required Skills

  • Terraform for AWS infrastructure provisioning
  • PySpark/Spark, Python, Advanced SQL
  • Lakehouse/Medallion Architecture (Bronze/Silver/Gold)
  • Type 2 Dimensions, CDC/Merge processing
  • Incremental & historical processing, deduplication, late-arriving data handling
  • Idempotency and resilient pipeline patterns
  • AWS data services (S3, Glue, EMR, Lambda, Step Functions, etc.)
  • Data modeling fundamentals & data quality mindset

Candidates Must Be Able to Explain

  • Updates/deletes from relational sources
  • Deterministic deduplication
  • Late-arriving/out-of-sequence data handling
  • Idempotent pipeline design
  • Merge keys, ordering columns, event time vs ingest time, soft deletes, reprocessing windows

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