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Tier-1 employer, common mid-level data-engineer skills, and metro location drive high applicant density.
Core data-engineering skills are transferable, but commercial CRM/finance integrations favor domain experience.
Explicit 4–5+ years plus mandatory stack (Python, SQL, Airflow, warehouse) and US hours increase strictness.
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Design, build, and maintain scalable data pipelines, data marts, and data architectures using SQL, Python, and orchestration tools to support analytics and data science initiatives.
Develop and optimize ETL/ELT processes and automate workflows integrating multiple commercial data sources including CRM and financial systems.
Lead complex data projects, support BI reporting (Power BI, Tableau), and translate business requirements into scalable technical data solutions while ensuring data quality and governance.
4–5+ years experience in data engineering or analytics engineering.
Strong proficiency in SQL (advanced queries, performance tuning) and Python (data processing, automation).
Experience with data warehousing technologies such as Redshift or AWS.
Flexible to work US hours.
Proven ability to bridge business and technology by transforming complex multi-source data into high-performance models and pipelines that enable data-driven decision-making.
Experienced in designing scalable data architectures and advanced data integration across sales, finance, and operational systems.
Skilled in managing end-to-end data projects in collaborative, cross-functional environments prioritizing data quality and stakeholder engagement.