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Senior Analytics Engineer

NALA
London, GBR
Full-time
Onsite
Discovered 5 days ago
dbtSnowflakeDatabricksPythonData modelingSemantic layers

Full Job Posting

About NALA

NALA builds cross-border payment products for consumers and businesses.

NALA's consumer app supports transfers from the UK, US, and EU to Africa and Asia.

Rafiki is NALA's B2B payments infrastructure, used by companies including MoneyGram and Western Union.

Role mission

Refactor the data warehouse for agent use and lower its cost.

Own the dbt and Snowflake transformation layer, making it dependable, well modeled, tested, documented, and trustworthy for people and agents.

Help develop a post-AI data stack with agent-readable artifacts, operable tools, owned context, and consistent company data.

Responsibilities

  • Own end-to-end modeling and transformation for assigned domains in dbt and Snowflake, from business question to governed model.
  • Set and uphold modeling methodology with the existing Analytics Engineer, including grain, dimensional patterns, and justified exceptions.
  • Make models agent-ready through rich semantics, consistent naming, business descriptions, and safe query behavior.
  • Own streaming and near-real-time pipelines alongside batch transformations, maintaining reliability and cost efficiency.
  • Establish coding and agentic-coding standards, systematic testing, and documentation as CI-enforced defaults.
  • Optimize warehouse performance and cost by addressing query patterns and materialization choices.
  • Deploy and supervise autonomous agents against the data stack; maintain monitoring, incident escalation, SLAs, and documented processes.
  • Support and mentor analysts in analytics-engineering practice.

Must-have requirements

  • At least three years of production dbt experience and ability to defend modeling methodology, including grain, facts, dimensions, and wide-table tradeoffs.
  • Production semantic-layer experience and ability to explain metrics, dimensions, grain, joins, and agent use.
  • Experience owning CDC or another stream, incremental transforms, and freshness-versus-cost decisions.
  • Experience with Snowflake or Databricks at scale, including refresh choices, clustering, and lag targets.
  • Experience scheduling, monitoring, and repairing pipelines in Airflow, Dagster, or an equivalent.
  • Production CI/CD and deployment management, including isolated validation, promotion, rollback or prevention of bad deploys, and success signals.
  • At least three years of production Python experience shipping pipeline transformations and checks.
  • Experience using frontier tooling to automate an analytics workload and describe its outcome and operation.

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