Responsibilities:

📍Design and implement scalable ingestion pipelines with a reusable and modular framework 📍Build robust, reusable frameworks to ingest data from internal sources (e.g., Prod DBs, cloud buckets, etc) and external APIs or files (e.g., CSVs, webhooks). 📍Ensure idempotency, backfill support, and error handling in pipeline design. 📍Optimize and own data warehousing, with clear table definitions, schemas, cost efficiency 📍Architect a centralized data lake/warehouse with clear schemas and partitioning strategies. 📍Support both batch and streaming workloads, and optimize for cost and performance. 📍Enable data discoverability, usability, and governance 📍Implement or integrate data cataloging and lineage tools 📍Define naming conventions, documentation standards, and ownership metadata to make data self-serve and intuitive for data scientists, analysts, and product / GTM teams. 📍Set up a mechanism for scalable access controls (with RBAC or ABAC), PII tagging, and data obfuscation. 📍Enable approaches for data quality checks, validation pipelines, and alerting for broken or stale data 📍Develop a strong understanding of how to use on-chain and off-chain data together

Required Qualifications:

📍5+ years experience in data engineering 📍Strong SQL and Python 📍Strong and informed opinions on data orchestrators, catalogs, governance, and testing frameworks 📍Experience combining in-house and external data

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