Data Engineer
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Job Description
We’re looking for a Mid Data Engineer who’s eager to build reliable, secure, and scalable data pipelines while actively adopting AI‑assisted engineering practices. You’ll work with Python, Snowflake, and cloud‑native services to support analytics, reporting, ML workflows, and data‑driven products.
You’ll operate with a DevSecOps mindset, treating security, testing, automation, and AI‑enabled productivity as first‑class citizens. This role expects continuous learning and thoughtful use of tools like GitHub Copilot to improve quality, speed, and maintainability—not to replace engineering judgment
What You’ll Do (Key Responsibilities)
• Data Pipelines & ETL/ELT
• Develop, schedule, and monitor ELT/ETL pipelines in Python to ingest data from APIs, files, and databases into Snowflake.
• Implement transformations (SQL & Python) following modular, reusable patterns.
• Leverage AI-assisted tooling (e.g., GitHub Copilot) to accelerate development, improve readability, and reduce boilerplate—while ensuring correctness through reviews and testing
• Snowflake Engineering
• Build tables, views, stages, and secure data shares.
• Tune queries and warehouse configs; manage roles, RBAC, and resource usage.
• Use AI assistance to analyze query performance, refactor SQL, and identify optimization opportunities
• Cloud & Orchestration
• Use cloud services (AWS/GCP/Azure) for storage, compute, and event triggers (e.g., S3/Blob/GCS; Lambda/Functions).
• Orchestrate jobs (Airflow/Cloud Composer/Prefect/Azure Data Factory) with proper alerting & SLAs.
• Apply AI tools to speed up pipeline scaffolding, DAG generation, and configuration validation
• DevSecOps & Reliability
• Apply CI/CD for data code (linting, unit/data tests, IaC review gates).
• Embed security scanning (dependencies, IaC policies), secrets management, and least‑privilege IAM.
• Contribute to observability (logging, metrics, lineage, data quality checks).
• Use AI responsibly to assist with test generation, documentation, and failure analysis
• Collaboration
• Work with Analytics/ML teams to productize models and enable self‑service.
• Write clear documentation (readme, runbooks, data dictionaries).
• Actively learn and upskill in AI‑driven engineering practices, sharing learnings with the team
Requirements
Function: General Business
Experience Level: Mid-Senior Level