We are seeking an analytical and strategic Data Engineering Leader to lead the modernization of a large-scale data platform. This role will oversee the redesign of data infrastructure, improve data accessibility and reliability, reduce duplication, and drive greater automation and efficiency.
The role will lead a small engineering team through a significant platform transformation, building a unified data environment that supports business users and downstream applications.
Core Responsibilities
Platform Architecture & Modernization: Lead the design and modernization of scalable data platforms, improving performance, reliability, and maintainability.
Data Pipeline & Ingestion: Oversee the ingestion, transformation, processing, and distribution of diverse internal and external datasets.
Data Quality & Governance: Build reliable data delivery and automated validation processes to ensure data accuracy, consistency, and governance.
Team Leadership: Lead and mentor a team of data engineers while establishing technical standards, engineering best practices, and delivery roadmaps.
Technology Strategy: Evaluate existing technologies and recommend modern architectural solutions, automation opportunities, and engineering practices.
Operational Improvement: Identify opportunities to eliminate manual processes, reduce system duplication, and improve overall platform efficiency.
Qualifications & Technical Profile
Engineering Leadership: Proven experience leading engineering teams, managing technical deliverables, and designing scalable data architectures. Hands-on technical leadership is expected at the Associate Director level, while the Director level emphasizes broader strategic oversight.
Modern Data Engineering: Strong experience with scalable data platforms, cloud technologies, data lakes/lakehouses, batch and streaming pipelines, and query/analytics platforms.
Problem-Solving & Automation: Strong analytical and problem-solving skills with a focus on automation, process optimization, and code-driven solutions.
Data Architecture: Experience designing and modernizing complex data ecosystems, including data ingestion, transformation, storage, and consumption layers.
Domain Experience: Experience in financial services, capital markets, or other data-intensive industries is a plus but not required.
Technology Flexibility: Ability to evaluate and work across different technology stacks, with emphasis on sound engineering principles rather than specific vendor tools.
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