Lead Analyst - Data Operations

Location: Nairobi, Kenya

About the role: As Lead Analyst – Data Operations, you will oversee the day‑to‑day running of the Data Desk, combining strong technical skills with hands‑on people management to maintain high standards of service, data accuracy, and team performance.

You will focus on three core priorities: keeping the request queue flowing smoothly, automating recurring work, and ensuring every data output meets Sun King’s quality standards. You will triage and route incoming L1/L2 tickets, review code and outputs from junior team members before they reach business stakeholders, and convert repeat requests into self‑service dashboards and automated solutions.

This is a highly visible role that works closely with the Head of Data Ops, Pod Coordinators, and the wider Data team. You will help shape how the Data Desk operates, refine and document its processes, and act as a key point of contact for stakeholders across Operations, Finance, and other functions.

The ideal candidate takes pride in operational accuracy, enjoys developing junior talent, and brings advanced SQL skills with a sharp eye for subtle errors in queries and data models. If you are motivated by improving how a technical service function runs — and by building the skills of the people within it — this role offers the opportunity to have a direct impact on how Sun King uses data to operate faster and more effectively. Ticket Routing & Triage:

• Oversee the incoming request queue and serve as the first point of triage for all L1/L2 tickets submitted to the Data Desk.
• Categorize requests across core service areas (e.g., query design, exports, operations, and finance) and assign each request to the appropriate pod coordinator or analyst without delay.
• Monitor SLA performance across the queue in real time, proactively flagging potential delays to the Head of Data Ops and relevant stakeholders.
• Maintain and regularly update the triage playbook, refining categorization rules as request patterns change and new ticket types emerge.

Automation of Routine Tasks:

• Identify recurring or high‑volume requests and assess opportunities to automate or move them to self‑service.
• Work with Pod Coordinators and Data Engineering to define requirements, design, and deliver self‑service dashboards and automated data pipelines.
• Ensure all automated outputs comply with Sun King’s data quality, governance, and documentation standards.

Technical Review & Code Quality:

• Review SQL queries, LookML configurations, and dashboard calculations produced by junior team members before delivery to business stakeholders.
• Check outputs for accuracy, performance, consistency with the Sun King Data Standard, and alignment with source‑of‑truth definitions.
• Provide clear, constructive feedback to strengthen code quality and build the team’s technical capabilities over time.

Team Operations & People Development:

• Run daily stand‑ups for the Data Desk team, ensuring clarity on priorities, ownership, and blockers.
• Hold regular one‑to‑one meetings with direct reports, tracking progress against professional development plans agreed with the Head of Data Ops.
• Onboard new graduates and team members into the Data Desk’s processes, tools, and quality standards as part of the Data Academy rotation curriculum.
• Support performance reviews by providing input on technical skills, operational reliability, and growth areas for team members.

Stakeholder Management & Continuous Improvement:

• Represent the Data Desk in cross‑functional forums, including sprint reviews and stakeholder check‑ins, when the Head of Data Ops is unavailable.
• Collect feedback from stakeholders on service quality, turnaround times, and data usability, and translate this into concrete improvement initiatives.
• Track and report on key operational metrics for the Data Desk (e.g., ticket volume, SLA adherence, automation coverage) and recommend actions based on trends.

• Hold a Bachelor’s or Master’s degree in a technical field such as Computer Science, Data Science, Statistics, Engineering or a related discipline.
• Bring 4–9 years of experience in data operations, business intelligence, analytics engineering, or a related technical function, including at least 1–2 years reviewing code, mentoring junior team members, or guiding a technical team.
• Have a proven track record of converting recurring manual tasks into automated processes or self‑service dashboards.
• Demonstrate strong practical SQL skills, with the ability to write, review, and optimize complex queries and identify subtle errors in joins, aggregations, and filters.
• Have working knowledge of LookML or equivalent BI semantic‑layer tools (e.g., Looker, Tableau, Power BI, Metabase) and familiarity with AWS‑based or similar cloud data environments.
• Have experience reviewing queries, dashboards, and other data outputs for accuracy, performance, consistency with source‑of‑truth definitions, and overall data quality.
• Can communicate technical concepts clearly to both technical and non‑technical stakeholders and provide practical guidance tailored to different levels of experience.
• Have experience coordinating workflows, monitoring SLAs, and maintaining high standards across a technical service or support function.
• Are comfortable balancing hands‑on technical work with queue management, team coordination, and people development responsibilities.