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Protocol Intelligence
Data-driven signals on your job's competitivenessTier-1 employer, metro Bangalore, mid-level generalist data role and hybrid setup increases competition.
Core data engineering skills are broadly transferable, though finance data experience slightly increases domain specificity.
Explicit 5+ years plus mandatory GCP/BigQuery, SQL/Python and production pipeline requirements raise selectivity.
Job Description
Structured overview of role & requirementsAbout This Role
Design, build, and maintain scalable, production-grade data pipelines and layered data platforms on GCP using BigQuery and medallion architecture.
Partner closely with business and finance stakeholders to frame data problems, deliver trusted data products, and support adoption across reporting and analytics.
Leverage generative AI and agentic tools to automate and improve data engineering delivery, including AI-native data solutions and lifecycle automation.
Minimum Requirements
5+ years of experience in data engineering, data warehousing, ETL, or ELT.
Hands-on experience with Google Cloud Platform (GCP) and BigQuery, strong SQL and Python skills for data engineering.
Strong understanding of modern data warehouse architecture, data integration, dimensional modeling, and production pipeline best practices including monitoring and error handling.
Experience working in Agile teams; work experience required: 5+ years; notice period: Not explicitly mentioned in the JD.
Ideal Candidate Profile
Experienced with implementing layered data platform patterns (e.g., medallion architecture) and developing semantic data models or analytical datasets.
Skilled in finance domain data (accounting, ledger, reconciliation) and able to translate finance business problems into scalable data solutions.
Practitioner of AI/ML tooling for data engineering automation, including prompt engineering, agent orchestration, and AI-enabled data products.