Data Engineer - Project Delivery Specialist

Data Engineer - Project Delivery Specialist at Deloitte, focusing on building and operating modern data products and platforms, including scalable batch and near-real-time data pipelines on AWS and Snowflake, developing transformation layers, implementing orchestration, and optimizing performance. Requires 7+ years of experience in data engineering with SQL and Python, cloud platforms, and data integration.

What you'd actually do

  1. Architect, build, and operate scalable batch and near-real-time data pipelines on AWS.
  2. Design robust ingestion patterns from source systems into S3 and into Snowflake.
  3. Develop transformation layers and curated datasets in Snowflake, including dimensional/data product modeling for analytics and downstream applications.
  4. Implement orchestration and workflow automation on AWS with retries, backfills, and idempotency.
  5. Build reusable Python components for ingestion, validation, and transformations; enforce standards via code reviews and testing.

Skills

Required

  • SQL
  • Python
  • Snowflake
  • PySpark
  • AWS
  • Azure
  • GCP
  • data integration frameworks
  • orchestration tools
  • Lakehouse/warehouse architectures
  • ELT patterns
  • DevOps principles
  • CI/CD pipelines
  • version control
  • Infrastructure-as-Code
  • data storage optimization
  • partitioning
  • file formats (Delta, Parquet)
  • performance optimization
  • data quality
  • data governance
  • metadata management
  • Bachelor’s degree in Computer Science, Information Technology, Computer Engineering, or related IT discipline; or equivalent experience

Nice to have

  • Agile delivery experience
  • Analytical ability to manage multiple projects

What the JD emphasized

  • 7+ years of experience as a Data Engineer delivering production-grade data pipelines and curated datasets.
  • 7+ years of hands-on experience with SQL and Python, including Snowflake and/or PySpark for scalable data processing and ELT.
  • 7+ years of experience designing, building, and operating batch and near-real-time data pipelines on cloud platforms (AWS preferred; Azure/GCP acceptable).