Design and build batch/streaming data pipelines for ingestion and analytics.
Own end-to-end delivery of complex data initiatives: architecture, deployment, testing, monitoring, and docs.
Evolve the data platform for large-scale processing using cloud-native tech.
Automate data operations: validation, quality checks, backfills, recovery workflows.
Optimize cost, performance, and reliability of data workloads.
Partner with Data Science, Product, and Engineering to translate requirements into solutions.

🎯 Requirements

5+ years hands-on data engineering building production-grade data platforms.
Python or Scala for data processing; SQL for analytics.
Apache Spark with performance tuning.
AWS data services (EMR, Lambda, S3) and data warehousing concepts.
Production pipelines operations: monitoring, alerting, incident response.
Strong software practices: version control, CI/CD, testing, code reviews.

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