Bloomberg

About Bloomberg Data:

Bloomberg runs on data, and in the Data department, we are responsible for acquiring, interpreting, and supplying data insights to our clients. Our Data teams work to collect, analyze, process, and publish the data which is the backbone of our iconic Bloomberg Terminal — the data which ultimately moves the financial markets! We’re responsible for delivering this data, news, and analytics through innovative technology — quickly and accurately.

The Team:

The Data Management Lab (DML) sits within the Data organization and supports Data’s pursuit of data management excellence by aligning industry best practices with Bloomberg's established expertise in financial market data. DML empowers our data professionals to make their products “ready-to-use,” through promoting increased data discoverability, accessibility, appraisability, interoperability, and analysis-readiness.

As a core component of the Quality Methods & Insights (QMI) group, the Process Engineering (PE) team supports the design and optimization of operational processes for our data manufacturing pipelines. We are passionate about how our people and technology work in concert, leveraging a suite of powerful tools built by infrastructure engineers. In partnership with Data Quality and Business Intelligence, we advance the Data Management Lab's mission by empowering data teams with advances in observability, instrumentation, and analysis to meet the evolving needs of Bloomberg's clients.

We are undergoing a strategic evolution, transitioning from a tactical implementation focus to a strategic enablement and empowerment role. We are seeking a visionary leader to manage our multi-disciplinary Process Engineering team, which includes Industrial Engineers and Process Systems Specialists.

As the Team Lead, you will be instrumental to this transformation. You will define the strategystrategies for how we analyze, govern, and innovate within our data manufacturing pipelines, leading a high-impact team that blends advanced analytical rigor with practical systems governance. The ultimate goal of this evolution is to deliver significant business impact through improved product quality, increased operational efficiency, and effective risk management.

You'll Be Trusted To:

• Set the Strategic Vision for Process Excellence: Define the team's roadmap for analyzing, governing, and innovating within our data manufacturing pipelines. Establish clear operating models with engineering, data, and product teams to drive effective collaboration.
• Build a Strategic Portfolio of Initiatives: Develop and manage a portfolio of process improvement initiatives, using systematic frameworks to prioritize efforts and ensure every project aligns directly with the core business goals of improved product quality, effective risk management, and increased operational efficiency.
• Lead and Mentor a High-Performing Team: Guide and develop a team of specialists in Industrial Engineering and Process Engineering. Foster a culture of innovation, methodological rigor, and shared ownership, empowering the team to become strategic enablers.
• Foster Deep Collaboration with QMI Partners: Work in close partnership with the Data Quality and Business Intelligence teams to create a unified approach to process excellence. Leverage their insights to inform process design and ensure your team's initiatives directly support data quality and analytical goals.
• Identify & Quantify Process Optimization Opportunities: Oversee deep-dive investigations to identify systemic bottlenecks and inefficiencies, specifically to identify opportunities to reduce effort through AI-driven automation or process re-design. Champion the use of advanced Industrial Engineering & Operations Research methodologies to quantify these opportunities and solve complex resource challenges.
• Drive Innovation Through Prototyping and Evaluation: Guide the prototyping of novel solutions to validate their impact and measure ROI before broader implementation. Lead the evaluation of new technologies and tools to enhance our operational capabilities.
• Oversee Process Governance and Knowledge Transfer: Establish and enforce best practices for our core workflow technologies (e.g., Jira). Promote knowledge sharing and develop training to federate process expertise across the organization.
• Act as a Key Strategic Partner: Interface with senior leadership in Data, Product, and Engineering. Translate complex business needs into a clear, actionable vision for process innovation and excellence, ensuring your team's work delivers measurable business impact.

You'll Need to Have:

*Please note we use years of experience as a guide, but we certainly will consider applications from all candidates who are able to demonstrate the skills necessary for the role.

• Deep expertise in advanced quantitative process analysis and Operations Research methodologies (e.g., simulation, optimisation, SPC, process mining).
• A proven track record of leading analytical or technical projects that deliver measurable business impact (e.g., in efficiency, quality, or risk management) from conception through to completion.
• Exceptional strategic thinking, problem-solving, and stakeholder management skills; able to thrive in a relationship-driven environment to establish credibility, influence senior leaders, and drive change across a large organization.
• 8+ years of relevant professional experience, including 3+ years in a formal leadership or management role, guiding technical or analytical teams.
• An advanced degree (MSc/PhD) in Industrial Engineering, Operations Research, Systems Engineering, or a related quantitative field.

We'd Love to See:

• Experience in an analytical, technical, or decision-science consulting role (or a similar internal consulting role), focused on operational or systems-level transformation.
• Professional certification in a process improvement methodology (e.g., Lean Six Sigma Black Belt, CMQ/OE, TOC).
• Professional certification in project management (e.g., PMP, PRINCE2, CSM).
• Experience applying these methodologies in complex operational environments such as manufacturing, supply chain, or logistics.
• Experience managing hybrid teams that include both deeply analytical (quantitative) and technical (systems-focused) specialists.
• A strong understanding of modern data stacks and how they can be leveraged to improve operational monitoring and analysis.