SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
NALA is a fintech company building cross-border payments infrastructure for the next billion users. Since 2022, the company has grown 120x, expanded from 9 to 150+ employees, raised $50M+ from top-tier investors, and was named to the Forbes Fintech 50 in 2025 and 2026. NALA operates two core products: a consumer app enabling cheaper, faster cross-border transfers from the UK, US, and EU to Africa and Asia, and Rafiki, a B2B payments infrastructure powering global giants like MoneyGram and Western Union.
As Senior Analytics Engineer, you will own the transformation layer in dbt and Snowflake, refactoring the warehouse for agents and cutting its cost. You are responsible for making the data stack dependable, well-modeled, tested, documented, and clear enough that both people and AI agents can trust the answers. You will set the data-engineering bar for the team and work alongside another analytics engineer.
Key responsibilities include: owning end-to-end modeling and transformation of business domains in dbt and Snowflake; setting and defending modelling methodology alongside the existing Analytics Engineer, including grain, dimensional patterns, and justified exceptions; making models agent-ready with semantic richness, consistent naming, business-term descriptions, and safety for autonomous querying; owning streaming and near-real-time pipelines (Kafka or similar) alongside batch transformation, keeping real-time flows reliable and cost-efficient; establishing and enforcing coding standards, systematic testing, and documentation as CI-enforced defaults; optimizing warehouse performance and cost by identifying and fixing expensive query patterns and materialization choices; deploying and supervising autonomous agents against the data stack while holding infrastructure to proper engineering standards with monitoring, incident management, and documented processes; and supporting and mentoring analysts on analytics-engineering practice to raise the engineering standard across the team.
You will help build the post-AI data stack: artifacts agents can read, tools agents can operate, owned context, and one reality the company can check. Success in the first three months means owning the transformation layer, identifying untrusted models and disagreeing numbers, ensuring changes are agreed and visible before deployment, making your reasoning followable without lengthy explanations, and delivering one automation a teammate can run with measurable time or volume impact. By six months, the team will be using the automation you taught them with demonstrated hours or volume savings.
The interview process includes a 30-minute talent team conversation, a 45-minute hiring manager deep dive on dbt and AI usage, a 2-3 hour take-home task (full AI tooling encouraged with prompts shared), a 1-hour technical presentation to the data team, and a 45-minute bar raiser conversation with leadership.
Benefits include private medical insurance, 27 days off plus UK bank holidays, birthday leave, enhanced parental leave (16 weeks primary, 4 weeks secondary), enhanced pension via salary sacrifice, 3x salary life insurance, global WeWork access, hybrid working (4 days/week in Canary Wharf office), $1000 annual learning budget, themed snacks and Friday lunches, monthly social events, and free barista-style coffee.
REQUIREMENTS:
Must-have:
- Three or more years of production dbt experience with ability to defend a modelling methodology, including Kimball concepts (grain, facts, dimensions, wide table trade-offs)
- Production experience with a semantic layer; ability to explain metrics, dimensions, grain, joins, and how agents read them
- Streaming and warehouse-scale experience: owned CDC or other streams, incremental transforms, and freshness/cost trade-offs; can distinguish when batch models are inappropriate for high-frequency numbers and what alternatives (streams, low-latency tables, semantic APIs) belong alongside the warehouse
- Production experience with Snowflake or Databricks, including cost and scale optimization (incremental vs. full refresh, clustering, lag targets)
- Orchestration experience: scheduled, monitored, and repaired pipelines in Airflow, Dagster, or equivalent
- CI/CD and deployment management: run projects where pull requests build changed models and downstream dependencies against production state, write to isolated schemas/clones, and promote after merge; can describe the path from development through CI to production, how bad deploys are stopped or rolled back, and success signals
- Three or more years of production Python experience shipping transforms and checks that run autonomously (not notebooks or take-home tests)
- Passion for building with and for agents: have used frontier tooling to automate traditional analytics workloads (recurring model changes, reconciliations, reports); can demonstrate outcomes in time or volume and explain observation, governance, and scaling beyond single-person laptop solutions
Nice-to-have:
- Ontology or business entity model on top of a semantic layer
- Distributed engine experience (Spark, Trino) on data exceeding single warehouse query capacity
- Fintech, payments, or regulated environment experience
