Lead Data Analyst (Data & Analytics)

About the job

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 judgment. • Become a recognized SME on 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 judgment 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 platforms.

Requirements

• DataOps
• SQL
• Data Analysis

Preferred Technologies

• DataOps
• SQL
• Data Analysis