Analytics Engineering Manager

The Role 

The Analytics Engineering Manager leads the Analytics Engineering team and is the senior technical leader for the horizontal platform initiatives that make Tala's data usable, trusted, and fast to work with across markets. This person has the authority to set priorities, the technical depth to lead the architecture, and the judgment to know when to build and when to delegate.

This is a hands-on management role: part people manager, part senior architect/builder. The person in this seat manages the Analytics Engineering team, oversees the quality and pace of its work, and acts as lead architect on selected platform initiatives, setting architecture and sequencing for work spanning Analytics Engineering and the data infrastructure they depend on. They are also expected to propose and drive changes that help the team deliver faster and more efficiently. When the work calls for it, they build alongside the engineers delivering it — not as additional throughput, but as the senior technical voice shaping how it gets built. Their time is split between leading the team, architecting selected initiatives, and building, and the balance shifts with what is blocking delivery.

What You'll Do:

Team Leadership and Delivery

• 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.

Platform Architecture and Technical Direction

• 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.

Hands-On Build

• 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.

AI-Native Analytics Workflows

• 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.

Technical Leadership

• 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.

What You'll Need:

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.

Technical Skills

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.

Judgment and Leadership Skills

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.

Mindset

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.