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Tier-1 brand, popular Data Engineer title, mid-level seniority, and broad PySpark/data pipeline requirements drive high competition.
Core data engineering skills transfer across industries, though banking controls and AVP leadership increase domain specificity.
Strong data engineering and leadership expectations but no explicit years or certifications, so moderate strictness.
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Build and maintain robust data architectures including data pipelines, data warehouses, and data lakes to ensure data accuracy, accessibility, and security.
Develop and deploy data processing and analysis algorithms handling complex data volumes and velocities, collaborating with data scientists on machine learning model deployment.
Lead or guide teams in delivering complex data engineering tasks impacting the broader business function, while managing operational risk and contributing to policy development.
Experience building and maintaining data pipelines, warehouses, and lakes with a focus on data security and consistency.
Technical proficiency with data processing technologies including PySpark (implied by job title).
Work Experience Required: Not explicitly mentioned in the JD.
Leadership experience or capability to lead collaborative technical assignments and influence decision-making expected at Assistant Vice President level (senior role).
Experienced data engineer comfortable operating at both architectural and implementation levels in complex, large-scale data environments.
Able to consult on complex data issues and communicate effectively with technical and non-technical stakeholders, including advising leadership.
Capable of leading teams or collaborative efforts with a focus on risk mitigation, control strengthening, and alignment with organizational objectives and strategy.