Manager, Data Services Operations

About the role

The role leads delivery and coordination for Data Services Operations projects at Mapbox. It defines measures of success and guides cross-functional teams to align with product and engineering goals. The position provides strategic direction and coaching while optimizing workflows for growth and efficiency. The hire will translate high-level objectives into concrete operational roadmaps and ensure that data services evolve in step with business demands. They will act as the primary liaison between technical teams and stakeholders to maintain clarity on priorities and timelines. This position is responsible for driving process improvements that make data workflows more reliable, scalable, and transparent. The role will monitor execution rigorously and escalate risks early so that leadership can maintain focused oversight. Success will be measured by the consistent and timely delivery of operational projects that enhance the stability and value of Mapbox data services.

Key facts

What you'll do

Operational projects advance on schedule and meet quality standards against timelines and requirements.

Outcomes and status reach the Head of Data Services Operations to support focused oversight and timely decisions.

Operational pipelines are analyzed to introduce AI solutions that reduce manual work and boost team productivity.

Operational bottlenecks are resolved and infrastructure scales to meet rising customer demand with urgent execution and continuous improvements.

Requirements

Bring 5+ years of leadership managing diverse service operational teams.

Show familiarity with GIS technologies or geospatial datasets as a strong advantage.

Own operational service KPIs and track their health as a regular responsibility.

Use data with a data-driven mindset to deliver outsized impact through direct analysis and management.

Demonstrate people management experience and strong communication and presentation skills.

Remain comfortable with ambiguity and maneuver effectively amid rapidly evolving priorities and workflows.

Write and speak Fluency in Russian, English (B2+), and hold a Bachelor's degree.

Nice to have

Experience with owning operational service KPI and tracking its operational health.

Practical notes

The position is based at Mapbox Minsk and requires onsite presence per local policy.

You will handle personal data during the application and employment process.

Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

The role operates within a technology stack that relies on data services, APIs, and SDKs.

Team culture emphasizes coaching, learning, and supporting diverse, high-performing individuals.

Work often involves balancing speed, accuracy, and cost in high-throughput environments.

Maps and geospatial datasets are central to the domain handled by this role.

Questions to ask

Good questions to ask the employer in the interview: what does success look like in the data services operations domain in the first six months, how is the team structured, what is the current biggest challenge facing the operations function, and how are decisions made at the intersection of data and product. Asking about growth paths for data roles, the review process for operational performance, and how the success of data services is measured over time is also well received. Employers expect substantive questions, and candidates who prepare specific inquiries demonstrate strong preparation and genuine interest in the function.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions and clearly communicate the implications for operations tend to advance fastest in this domain. Understanding how to align data service metrics with product outcomes will distinguish high performers. The ability to manage priorities in fast-moving environments while maintaining rigorous standards is a critical long-term skill. Strong written and verbal communication will remain central as the role interfaces heavily with stakeholders and technical specialists. This position is an opportunity to build a durable foundation in data services operations within a high-impact mapping and location data organization.