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Strong corporate brand and metro locations increase applicant density, but senior principal level reduces competition.
Core data engineering skills are broadly transferable across industries despite financial domain context.
Requires hands-on data engineering, cloud platforms, and SQL without explicit years, so moderate strictness.
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Translate business requirements into technically actionable, build-ready data engineering stories with clear acceptance criteria.
Collaborate closely with product owners, data engineers, and architects to ensure technical feasibility and align product intent with architectural standards on cloud data platforms.
Contribute hands-on to data analysis, SQL querying, schema design, pipeline validation, and embedding data quality and automation into workflows.
Strong hands-on experience with SQL and analyzing large datasets.
Solid understanding of cloud data platforms (AWS, Azure, or GCP) and modern data architectures including data lakes, ELT/ETL pipelines, and streaming or batch processing.
Experience working in Agile/DevOps delivery models and familiarity with APIs, data modeling, and data quality frameworks (e.g., SODA).
Work Experience Required: Not explicitly mentioned in the JD.
Technical collaborator comfortable bridging product and engineering teams with deep technical involvement in data platforms and cloud environments.
Experienced in engineering-driven delivery models with prior exposure in data engineering, analytics engineering, or platform engineering teams.
Ability to reduce ambiguity and improve delivery predictability through high-quality technical documentation and continuous process improvement.