Responsibilites

📍Lead the architecture, design, and implementation of data infrastructure and pipelines, spanning both batch and real-time / streaming workloads 📍Build and maintain scalable, efficient, and reliable ETL/ELT pipelines using languages and frameworks such as Python, SQL, Spark, Flink, Beam, or equivalents 📍Work on real-time or near-real-time data solutions (e.g. CDC, streaming, micro-batch) for use cases that require timely data 📍Partner with data scientists, ML engineers, analysts, and product teams to understand data requirements, define SLAs, and deliver coherent data products that others can self-serve 📍Establish data quality, validation, observability, and monitoring frameworks (data auditing, alerting, anomaly detection, data lineage) 📍Investigate and resolve complex production issues: root cause analysis, performance bottlenecks, data integrity, fault tolerance 📍Mentor and guide more junior and mid-level data engineers: lead code reviews, design reviews, and best-practice evangelism 📍Stay up to date on new tools, technologies, and patterns in the data and cloud space, bringing proposals and proof-of-concepts when appropriate 📍Document data flows, data dictionaries, architecture patterns, and operational runbooks

Minimum Qualifications:

📍8+ years of experience in data engineering (or similar) roles 📍Strong experience in ETL/ELT pipeline design, implementation, and optimization 📍Deep expertise in Python and SQL writing production-quality, maintainable, testable code 📍Experience with large-scale data warehouses (e.g. Databricks, BigQuery, Snowflake) 📍Solid grounding in software engineering fundamentals, data structures, and systems thinking 📍Hands-on experience in data modeling (dimensional modeling, normalization, schema design)

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