Lead Data Analyst (Data & Analytics)

Lead Data Analyst (Data & Analytics) Key Responsibilities Team Leadership & Coordination Lead and coordinate the activities of the DataOps analyst team, ensuring clear ownership, accountability, and coverage. Establish and run effective triage processes for incoming data issues, incidents, and requests. Ensure analysts are working on the right problems at the right time, balancing urgent support needs with longer-term data projects. Provide coaching, feedback, and mentorship to analysts, supporting both technical growth and operational judgement. Become a recognized SME ’s core data flows Triage, Workflow & Prioritisation Define, implement, and continuously refine workflows for: issue intake and triage Investigation and resolution Escalation to Data Engineering or other teams Communication and closure Act as the primary point of coordination for high-severity or cross-team data issues. Ensure priorities are clearly understood and agreed with stakeholders, and that trade-offs are made explicitly.

Operational Effectiveness & Service Quality Monitor team effectiveness using qualitative and quantitative signals (e.g. response times, backlog health, recurring issues). Identify bottlenecks, failure modes, and areas of operational risk within the data support process. Drive initiatives to improve reliability, transparency, and predictability of DataOps outcomes. Ensure documentation, runbooks, and knowledge sharing are maintained and actively used. Stakeholder & Partner Management

Partner closely with Customer Experience, Customer Support, Product, Partnerships, and Data Engineering to align expectations and delivery. Provide clear, timely communication to stakeholders on issue status, risks, and timelines. Represent DataOps in cross-functional discussions about data quality, supportability, and operational readiness. Tooling & Continuous Improvement Ensure the team effectively uses existing tooling, dashboards, and workflows to deliver data support. Identify gaps in tooling or process and drive the creation of new lightweight tools, metrics, or workflows where appropriate. Collaborate with Data Engineering on requirements for more robust or systemic solutions. Champion a culture of continuous improvement, learning, and operational excellence. Essential Criteria

Background in financial services or working with trading, securities, or regulatory data. Experience leading or coordinating a data operations, data support, or analytics team in an enterprise or SaaS environment. Strong understanding of data pipelines, ETL concepts, and analytical data platforms (without necessarily owning their implementation). Strong SQL skills and experience working with large or complex datasets, and experience in scripts in a common language such as Powershell or Python. Demonstrated experience establishing triage processes, workflows, or operational cadences. Strong stakeholder management skills, with the ability to balance competing priorities and communicate trade-offs clearly. Proven ability to improve team effectiveness through better processes, tooling, or prioritization. Comfortable operating in a fast-moving environment with ambiguous or incomplete information. Sound judgement around escalation, risk, and impact. Desirable Criteria Prior experience managing analysts in a data support or data product environment. Exposure to data quality frameworks, operational metrics, or service management practices. Experience partnering closely with data engineering teams on systemic improvements. Familiarity with incident management or on-call support models for data