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from requirements through delivery and iteration\nDevelop and optimize SQL queries, materialized views, and pre-aggregated summaries to keep dashboard latency and cost in check\nBuild ETL and ELT processes for ingesting, transforming, and validating data from multiple sources\nBuild and maintain gold and published-layer datasets for analytics and BI consumption\nPartner with analytics and BI teams to prepare curated datasets for embedded analytics (e.g., Omni) and BI dashboards (e.g., Power BI)\nMonitor data pipeline health, troubleshoot failures, and implement alerting and observability solutions\nEnsure data quality, accuracy, and consistency across all pipeline stages\nParticipate in code reviews, documentation, and knowledge sharing with the team\nRequirements\nRequired Qualifications\n3-7 years of professional data engineering experience\nStrong proficiency in SQL; demonstrated ability to write and optimize complex queries\nHands-on experience with Databricks; working knowledge of Apache Spark fundamentals\nSolid understanding of data warehouse and data lake concepts and architecture patterns\nExperience designing and implementing ETL/ELT pipelines\nProficiency in at least one programming language (Python, Scala, or similar)\nExperience with AWS (or another major cloud) - 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