Analytics Engineering Manager
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 1d ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 53d ago
The date the source published, not the day we noticed it (2026-09-09). Last seen at its source 1h ago.
We have tracked this listing since 11 Sep 2026 (51 days). The employer's own board has carried it every time we have read it, most recently 1 hour ago.
Is it remote?
Marked remote on the employer's board
Their board carries a remote setting on this posting — a field they filled in, not wording we read. The location field names somewhere specific, which is usually where the team or the entity sits.
Who may apply?
United States
The description states no restriction of its own. This is the source's own tag.
Pay not stated
Similar roles pay $177.9k–243.5k/yr
Middle 50% of 278 listings that do state pay — Engineering · Manager · United States · USD/year. This employer has published no salary; this is what comparable listings we hold disclose, never converted between currencies or periods. How this is calculated.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
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Lever employer's own board first seen 51d ago · last seen 1h ago
The listing
The company has been named to the Fortune Impact 20 list, CNBC’s World’s Top Fintech Companies twice, CNBC Disruptor 50 for five years, and Forbes’ Fintech 50 list for ten years running.
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:
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8+ years in analytics engineering, data engineering, or data platform roles, including at least 2+ years of managing engineers directly.
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Demonstrated ownership of a semantic layer, metrics layer, or equivalent governed-metric platform at organizational scale.
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Track record of shipping horizontal data infrastructure across different business units.
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Experience acting as the end-to-end "glue" across data generation, transformation, consumption, and metric definition, rather than owning one narrow slice.
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Comfort operating in a hybrid manager / senior architect / hands-on builder shape and switching modes based on what is actually blocking the work.
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Track record of managing and developing engineers: setting priorities, giving feedback, hiring, and keeping delivery predictable.
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Expert SQL and strong Python.
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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.
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Hands-on experience with semantic layer technologies — warehouse-native semantic models, BI semantic layers, headless BI APIs, or equivalent.
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Working knowledge of LLM and agent tooling applied to analytics workflows.
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Experience with modern BI and notebook environments (Omni, Looker, Hex, Databricks, Tableau or similar).
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Data observability and quality tooling; automated testing and CI/CD for data.
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Able to make architecture calls under ambiguity, document the reasoning, and hold the line against expedient workarounds.
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Able to influence senior technical and business peers
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Able to coach engineers, give direct feedback, and hold a high bar while keeping the team motivated
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Strong written and verbal communication; comfortable producing the design docs and decision records that make direction durable.
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Balances stakeholder demand against the real cost of producing and storing data.
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Bias toward shipping. Discovery is a means to delivery, not a deliverable.
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Willing to be persistent about follow-through when initiatives lose momentum.
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Treats tech debt and small daily quality-of-life wins as legitimate priorities, not chores.
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Anticipates what data consumers will need before they ask.
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Comfortable being the newest person in the room and still making the call.
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Measures success by what the team ships rather than by personal output, and delegates readily.