Responsibilities: Architect and develop end-to-end data pipelines - from ingestion to transformation to consumption Lead solutioning and integration for complex data workflows (batch and streaming) Use AI-assisted coding tools (e.g., GitHub Copilot, Claude, Gemini) to accelerate code development, refactoring, and debugging Implement robust data quality, testing, lineage, and governance frameworks Drive best practices across pipeline performance, reusability, and scalability Mentor junior engineers and contribute to capability building within the data team Requirements 6+ years of experience in data engineering, with expertise in: End-to-end pipeline development (batch and streaming) Data modeling (dimensional, Data Vault, OBT) ETL/ELT design patterns, performance tuning, and optimization SQL (Advanced) and Python (Advanced) Apache Spark for large-scale data processing Proficiency using AI coding tools (e.g., Copilot, Claude, Gemini) to enhance productivity and code quality Strong understanding of data quality frameworks, unit testing, and CI/CD for data workflows
Preferred
Qualifications: Experience with Google Cloud Platform services: BigQuery, Dataflow, Cloud Composer, Pub/Sub, Dataproc, Vertex AI Exposure to finance or sales data domains Familiarity with Databricks, Delta Lake, or Apache Iceberg GCP Professional Data Engineer certification is a plus
What
We Offer: Opportunity to work on modern data platforms with GenAI integration Access to professional development support and cloud certification sponsorship Competitive compensation and flexible work arrangements A fast-paced, high-impact environment where innovation is valued

