Analytics Engineering Manager

Join Tala as an Analytics Engineering Manager, where you will lead the Analytics Engineering team and drive key horizontal platform initiatives. This hands-on management role requires a blend of people management, architecture, and building. You will oversee the team's delivery, lead the architecture for key projects, and build production data assets when necessary. Additionally, you will define and set standards for AI-native analytics workflows and raise the technical bar through design review and documentation.

Lead and manage the Analytics Engineering team, overseeing the quality and pace of its work, and acting as lead architect on selected platform initiatives.

Set priorities, lead the architecture, and make decisions on whether to build or delegate, ensuring the team delivers faster and more efficiently.

Treats tech debt and small daily quality-of-life wins as legitimate priorities, not chores

Bias toward shipping. Discovery is a means to delivery, not a deliverable

About Tala

Measures success by what the team ships rather than by personal output, and delegates readily

Responsibilities

• Drive the adoption of AI in daily workflows, defining and setting standards for how Tala's Analytics functions use AI in their processes.
• Strong written and verbal communication; comfortable producing the design docs and decision records that make direction durable
• Track record of managing and developing engineers: setting priorities, giving feedback, hiring, and keeping delivery predictable
• Able to make architecture calls under ambiguity, document the reasoning, and hold the line against expedient workarounds
• Track record of shipping horizontal data infrastructure across different business units

Requirements

• 8+ years in analytics engineering, data engineering, or data platform roles, including at least 2+ years of managing engineers directly
• Data observability and quality tooling; automated testing and CI/CD for data

Able to influence senior technical and business peers

• Anticipates what data consumers will need before they ask
• Demonstrated ownership of a semantic layer, metrics layer, or equivalent governed-metric platform at organizational scale
• Able to coach engineers, give direct feedback, and hold a high bar while keeping the team motivated
• Hands-on experience with semantic layer technologies — warehouse-native semantic models, BI semantic layers, headless BI APIs, or equivalent
• Balances stakeholder demand against the real cost of producing and storing data
• Comfortable being the newest person in the room and still making the call
• Working knowledge of LLM and agent tooling applied to analytics workflows
• Comfort operating in a hybrid manager / senior architect / hands-on builder shape and switching modes based on what is actually blocking the work
• Willing to be persistent about follow-through when initiatives lose momentum
• Experience with modern BI and notebook environments (Omni, Looker, Hex, Databricks, Tableau or similar)
• Experience acting as the end-to-end "glue" across data generation, transformation, consumption, and metric definition, rather than owning one narrow slice
• Expert SQL and strong Python
• Deep experience with a cloud data warehouse (Snowflake preferred) and with dbt or equivalent transformation frameworks, including incremental strategy, testing, and performance and cost optimization

Pay

USD 160,000 - 200,000