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Metro location, generalist data-engineer title, and mid-tier employer increase applicant competition.
Core data engineering skills transfer across industries, though finance domain familiarity is beneficial.
No explicit years but senior title and required technical stack make filters moderately strict.
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Translate business outcomes into clear, technically actionable requirements for cloud data platform initiatives.
Collaborate with engineering teams to ensure technical feasibility, design alignment, and implementation readiness, including hands-on contribution to data analysis and pipeline validation.
Define and embed data quality and reliability metrics, supporting automation of validation and delivery process improvements.
Strong hands-on experience with SQL and data analysis on large datasets.
Solid understanding of cloud data platforms such as AWS, Azure, or GCP.
Experience working with modern data architectures, ELT/ETL pipelines, and data quality frameworks.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in working closely with product owners and engineering-heavy teams within cloud data or analytics environments.
Comfortable with Agile/DevOps delivery models and able to translate complex technical concepts for diverse audiences.
Background or experience bridging between traditional Business Analyst roles and modern data engineering or platform engineering teams.