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Mid-level Data Engineer title, metro location, and 5-8 years make applicant competition high.
Core data engineering tools and patterns (PySpark, Synapse, SQL) are easily transferable across industries.
Explicit 5-8 years plus Azure/PySpark stack and certification expectations make filtering stringent.
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Build, maintain, and optimize scalable data pipelines and ETL/ELT processes ingesting data from 1-3 source systems to support analytics and AI solutions.
Collaborate with data product managers and teams to gather requirements, implement data security, ensure data quality and performance, and deliver well-documented data products.
Investigate and resolve data pipeline issues, support continuous process improvements, and optimize performance and cost-effectiveness of data engineering solutions.
Bachelor’s degree in Mathematics, Statistics, Computer Science, Data Analytics/Science, or related field.
5-8 years of data engineering experience, including coding in PySpark or similar languages and building data pipelines.
Microsoft Certified: Azure Data Engineer Associate (DP-900) or Fabric Data Engineer Associate or related cloud certifications.
Experience with SQL, data modeling, ETL/ELT development, data privacy fundamentals, and Git-based workflows.
Experienced working with Azure Synapse, PySpark, APIs, and cloud data engineering tooling (Azure Data Factory, Azure DevOps) with some knowledge of medallion architecture.
Able to manage multiple priorities, independently troubleshoot, and optimize data process performance and costs.
Collaborates effectively with data scientists, AI teams, and business stakeholders to translate business requirements into robust data products.