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Tier-1 brand, mid-level popular Data Engineer role, metro location, and broad skill requirements drive high competition.
Core data engineering skills are transferable, but financial-services domain preference makes background fit sensitivity medium.
Explicit 3–6 years, required SQL/Python, modern data stack and financial-domain experience make shortlisting strict (high).
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Own end-to-end data pipelines and analytics-ready data models supporting predictive analytics workflows and recurring reporting deliverables such as monthly dashboards and quarterly attrition reporting.
Ensure data quality through validation, version control, and automated controls while transitioning manual reports to scalable automated solutions.
Collaborate with stakeholders and translate business requirements into reusable dataset definitions, clear documentation, and scalable reporting assets with operational readiness and process documentation ownership.
3–6 years of experience in data engineering, analytics engineering, or related reporting roles within financial services or data-driven environments.
Proficiency in SQL and Python; experience with data visualization/reporting tools such as Power BI.
Bachelor’s degree in Computer Science or quantitative field (Math, Stats, Data Science); advanced degree preferred.
Work onsite at least 3 days/week in India with specified timings (2:00pm–10:30pm) as per role requirements.
Experienced in building and maintaining reusable analytics datasets and productionized workflows with strong data validation and control discipline.
Skilled in analytics engineering best practices including modular SQL/Python code, version control, testing, and operational documentation.
Capable of translating business analytics needs into governed datasets and metrics with proven stakeholder management and communication skills, especially in financial services or related domains.