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Mid-level, generalist Data Engineer role in metro with known financial brand yields high applicant competition.
Core analytics engineering skills are transferable, though financial services domain experience increases relevance.
Explicit 3–6 years plus mandatory SQL/Python and financial-services analytics engineering experience increases filter strictness.
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Own end-to-end data pipelines and recurring analytics deliverables for predictive analytics workflows and performance tracking, ensuring data accuracy through strong controls and automation.
Translate business requirements into governed, reusable datasets, metric definitions, and scalable reporting, managing stakeholder collaboration and communication.
Apply analytics engineering best practices including modular code, version control, testing, documentation, and maintain operational readiness via runbooks and process documentation.
3-6 years of experience in data engineering, analytics engineering, reporting, or related roles preferably within financial services or data-driven environments.
Proficiency in SQL and Python; experience with data visualization tools like Power BI.
Working knowledge of modern data stack concepts including ELT, data warehouses/lakehouses, dimensional modeling, data lineage, and version-controlled analytics code.
Ability to manage recurring deliverables with timelines, documentation, and stakeholder communication; strong written communication skills.
Experienced in building reusable data products with strong data quality controls and automation focus to reduce manual reporting.
Capable of partnering effectively across business, technology, data engineering, and modeling teams to translate requirements into scalable analytic solutions.
Comfortable working in a hybrid office model with stakeholder-facing responsibility for recurring analytics in a financially regulated environment.