Senior Data Management Professional - Data Engineering (Shared Infrastructure) Location New York Business Area Data Ref # 10051819 Description & Requirements What9s the role? As a Data Engineer on the Shared Infrastructure team, you will play a central role in shaping the foundation for how data workflows are built, scaled, and operated across the organization. You will design and develop shared components, workflow patterns, and developer-facing systems that enable teams to deliver data pipelines with greater consistency, efficiency, and reliability. You will define and implement reusable libraries, templates, and reference architectures for core workflows, including data ingestion, transformation, evaluation, and annotation, establishing common standards that reduce fragmentation and accelerate development across a distributed set of teams. In addition, you will contribute to the evolution of emerging capabilities, such as automated evaluation and LLM-enabled workflows, partnering closely with engineering teams to help integrate and scale these approaches within production environments. This role is critical to advancing a more unified, scalable, and maintainable data ecosystem, shifting the organization from bespoke, one-off solutions toward a coherent, systems-driven approach to data and AI workflow development. We9ll Trust You To: - Design and build reusable data pipelines, libraries, and workflow components supporting annotation and evaluation workflows that can be adopted across teams rather than one-off solutions for a single use case - Contribute to and integrate with automated evaluation frameworks and LLM-enabled annotation workflows in partnership with AI Engineering teams, creating scalable patterns for data generation, validation, and quality measurement - Collaborate on integrations and automation between data systems and LLM services, ensuring solutions are practical, cost-aware, and aligned with engineering constraints - Implement monitoring and observability patterns that help teams detect data quality issues, workflow failures, and performance bottlenecks, including those specific to LLM-driven workflows - Create reference implementations, templates, and tooling that improve developer experience and make it easier for teams to adopt shared patterns - Identify opportunities to reduce manual effort and fragmentation, and implement scalable automation and shared solutions that deliver value across multiple teams - Partner closely with engineering teams to translate prototypes into production-ready capabilities, contributing to designs that can be reliably deployed and maintained - Work directly with data teams to understand pain points, gather feedback, and drive adoption of shared solutions across the organization You9ll Need To Have: - Strong proficiency in Python and SQL, with experience building data pipelines, automation, and analytics workflows - At least 4+ years of professional experience in data engineering, analytics engineering, workflow automation, or a closely related technical role - A bachelor9s degree or above in Statistics, Computer Science, Quantitative Finance or other STEM related field or degree-equivalent qualifications - Experience working with object stores (e.g., S3), relational databases (e.g., Postgres), data modeling, and pipeline orchestration in production or near-production environments - Experience building data validation, monitoring, or observability solutions to ensure data quality and workflow reliability - Experience developing reusable components, libraries, or workflows, with an understanding of how to design solutions that can scale across multiple use cases - Ability to operate effectively in ambiguous or evolving environments, translating loosely defined problems into practical, scalable solutions - Proven ability to work cross-functionally with engineering, data, and product stakeholders to deliver solutions that are both technically sound and broadly usable - Strong written and verbal communication skills, including the ability to document systems, define patterns, and explain technical trade-offs clearly We9d Love To See: - Experience with LLM-enabled workflows, annotation pipelines, or AI-driven data processes - Familiarity with evaluation frameworks, dataset quality measurement, or approaches to validating model or data outputs - Experience improving fragmented or manual workflows through standardization, automation, and reusable tooling - Exposure to dataset versioning, workflow instrumentation, and data quality monitoring best practices - Experience building shared tools, internal libraries, or systems used across multiple teams - Experience partnering with engineering teams to scale prototypes into production-ready systems - Familiarity with internal tools such as BBGithub, BCOSv2/BCS, BPaaS, QlikSense, DSP, or similar platforms Salary Range = 110,000 - 190,000 USD Annual + Benefits + Bonus The referenced salary range is based on the Company9s good faith belief at the time of posting. Actual compensation may vary based on factors such as geographic location, work experience, market conditions, education/training and skill level. We offer one of the most comprehensive and generous benefits plans available and offer a range of total rewards that may include merit increases, incentive compensation (exempt roles only), paid holidays, paid time off, medical, dental, vision, short and long term disability benefits, 401(k) +match, life insurance, and various wellness programs, among others. The Company does not provide benefits directly to contingent workers/contractors and interns.