Data Engineer

Key Responsibilities Data Engineering & Pipeline Development • Design, build, and maintain robust ETL/ELT pipelines to ingest, transform, and deliver data for reporting use cases • Develop curated data layers (e.g., bronze/silver/gold) optimized for BI consumption • Ensure high data quality through validation, monitoring, and automated checks Reporting Infrastructure Enablement • Build and optimize data models to support scalable reporting • Partner with analytics teams to translate business requirements into performant data structures • Enable self-service reporting by creating well-documented, reusable datasets Platform & Performance Optimization • Improve performance of data pipelines and queries for large-scale datasets • Implement efficient partitioning, indexing, and query optimization strategies • Support cloud-based environments (e.g., Azure, AWS, or GCP) Data Governance & Reliability • Establish standards for data lineage, documentation, and metadata management • Ensure adherence to data governance, security, and compliance practices • Monitor pipelines and troubleshoot issues proactively Required Qualifications • 4–8+ years of experience in data engineering or data platform development • Robust proficiency in SQL, Python, and AWS • Experience building pipelines with tools such as: • Azure Data Factory, Databricks, Airflow, or equivalent • Experience with data warehousing solutions (e.g., Snowflake, Synapse, Redshift, BigQuery) • Robust understanding of data modeling concepts (star schema, dimensional modeling) • Experience supporting BI/reporting tools (e.g., Power BI, Tableau) Preferred Qualifications • Experience building reporting infrastructure from scratch or during organizational expansion • Familiarity with data contracts and data product thinking • Exposure to DevOps and CI/CD pipelines for data (Git, automated deployments) • Experience working in distributed/global teams