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Popular mid-level Data Engineer title and 5+ years draw applicants, though specialized stack reduces competition.
Core data engineering skills transferable, but retail/CDP and platform-specific expertise increases domain sensitivity.
Multiple mandatory platforms, 5+ years requirement, and AI-assisted engineering mandate create strict filtering.
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Lead design, development, and optimization of scalable ETL/ELT data pipelines processing billions of retail data records across multi-cloud platforms (Databricks, Snowflake, AWS, Azure).
Own CI/CD pipeline enhancements including Git workflows, automated testing, containerized deployments with Docker and Kubernetes, and monitoring.
Establish and enforce best practices in data governance, quality, and security while mentoring and leading the data engineering team.
Minimum 5 years experience in Data Engineering.
Proven expertise building and managing large-scale Data Warehouses and Data Lakes from scratch.
Hands-on experience with Change Data Capture (CDC), batch and streaming data processing.
Advanced skills with SQL, Python programming, REST API development, Apache Airflow, Airbyte, and Git-based CI/CD pipelines.
Versatile engineer proficient in multiple cloud data platforms and able to lead technical teams on cross-platform, scalable data solutions.
Experience integrating DevOps and MLOps practices into data engineering workflows to support ML lifecycle and production readiness.
Demonstrated ability to innovate by leveraging AI-assisted coding and staying current with trends in data engineering, GenAI, and observability tools.