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Tier-1 brand, mid-level generalist data role in a metro with broad skill requirements increases applicant competition.
Core data engineering skills (SQL, Python, ETL, cloud) are broadly transferable across industries, so sensitivity is low.
Explicit 2–5 years requirement plus mandatory data-engineering skills and MSBI/PowerBI drives high filtering strictness.
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Build and operate reliable, scalable data pipelines for analytics and reporting, ensuring performance, correctness, and maintainability.
Develop data transformations and models for analytics-ready datasets with consistent definitions and lineage tracking.
Implement data quality checks, observability, automation, and support production operations including incident management and root cause analysis.
2-5 years of professional experience in Data Engineering.
Proficient in building and maintaining data pipelines including ingestion, transformation, and publication of curated datasets.
Experience with complex SQL queries and at least one programming language used in data engineering (e.g., Python, Scala, Java).
Experience with big data ecosystems, cloud technologies, MSBI stack (SSIS, SSAS, SSRS), and MS Power BI.
Technical focus on reliable, production-grade data systems with strong software engineering practices (version control, code review, CI/CD).
Ability to work collaboratively with both technical and non-technical stakeholders to deliver reusable, trusted datasets.
Proactive in continuous improvement through automation and standardization of data delivery workflows.