Analytics Engineering Manager

Tala is hiring an Analytics Engineering Manager to lead its Analytics Engineering team and advance the horizontal data platform across markets. The role combines people leadership, platform architecture, hands-on delivery, and the development of AI-native analytics workflows that make data usable, trusted, and efficient to work with.

Responsibilities:

• Lead, coach, and develop the Analytics Engineering team: set clear expectations, run planning and prioritization, give regular feedback, and support hiring, performance, and career growth
• Oversee the team's delivery: review designs and code, track commitments against dates, and raise risks and trade-offs early
• Propose and drive ways for the team to go faster and spend less effort per delivery, such as better tooling, automation, shared standards, and AI-assisted workflows, and see the adopted ones through to completion
• Decide where to build directly, where to delegate, and where to step in as lead architect, based on what is actually blocking delivery
• Lead the architecture for key horizontal projects in close partnership with the team. Accountable for taking initiatives to the finish line
• Make and document the sequencing calls on team initiatives: what gets finished, what gets deferred, and what gets stopped
• Provide senior technical review on architecture decisions before Tala commits engineering capacity to them
• Design for governance and compliance from the outset (Tala operates under distinct regulatory regimes)
• Clear dependency chains and unblock ICs across teams by removing tech debt, resolving ambiguity, and forcing decisions to closure
• Build production data assets, services, and tooling directly when it is the fastest way to unblock delivery; such as models, pipelines, SDKs or agent harnesses
• Work alongside the Analytics Engineering team's engineers on shared initiatives, adding senior capacity to work that is currently single-threaded through individual engineers
• Own end-to-end delivery of specific platform assets, whether built directly or by the team
• Define and set standards for how Tala's Analytics functions use AI in daily workflow — code generation, model development, documentation, testing, and self-serve analysis
• Build the context and guardrail layer that lets AI agents query governed metrics safely, so capability and compliance ship together rather than sequentially
• Move the organization from experimentation to adopted practice with documented patterns and best practices
• Raise the technical bar through design review, pairing, and documented standards
• Reduce friction to access and understand data; treat data education and documentation as core deliverables, not afterthoughts
• Develop senior engineers into technical leads so that architecture ownership spreads further across the team

Requirements:

• 8+ years in analytics engineering, data engineering, or data platform roles, including at least 2+ years of managing engineers directly
• Demonstrated ownership of a semantic layer, metrics layer, or equivalent governed-metric platform at organizational scale
• Track record of shipping horizontal data infrastructure across different business units
• Experience acting as the end-to-end "glue" across data generation, transformation, consumption, and metric definition, rather than owning one narrow slice
• Comfort operating in a hybrid manager / senior architect / hands-on builder shape and switching modes based on what is actually blocking the work
• Track record of managing and developing engineers: setting priorities, giving feedback, hiring, and keeping delivery predictable
• 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
• Hands-on experience with semantic layer technologies — warehouse-native semantic models, BI semantic layers, headless BI APIs, or equivalent
• Working knowledge of LLM and agent tooling applied to analytics workflows
• Experience with modern BI and notebook environments (Omni, Looker, Hex, Databricks, Tableau or similar)
• Data observability and quality tooling; automated testing and CI/CD for data
• Able to make architecture calls under ambiguity, document the reasoning, and hold the line against expedient workarounds
• Able to influence senior technical and business peers
• Able to coach engineers, give direct feedback, and hold a high bar while keeping the team motivated
• Strong written and verbal communication; comfortable producing the design docs and decision records that make direction durable
• Balances stakeholder demand against the real cost of producing and storing data
• Bias toward shipping. Discovery is a means to delivery, not a deliverable
• Willing to be persistent about follow-through when initiatives lose momentum
• Treats tech debt and small daily quality-of-life wins as legitimate priorities, not chores
• Anticipates what data consumers will need before they ask
• Comfortable being the newest person in the room and still making the call
• Measures success by what the team ships rather than by personal output, and delegates readily
• Deep experience with a cloud data warehouse (Snowflake preferred)