Data Engineer / Analyst

Builder expectation

You will not only write queries and maintain dashboards. You are expected to build the data platform itself: design and ship the pipelines, models, metrics, and reports that the company and its customers run on, use AI agents to accelerate every step, and own the numbers end to end, from raw event to the figure a CFO acts on.

The role

You will be Bluefyn's first dedicated data hire and will own the data platform end to end, spanning data engineering, analytics, and applied data science, with AI and coding agents in your daily work. This is a hands-on builder role, not a dashboard-maintenance position. As the company grows, you will define the data function, hire the team, and lead it while remaining hands-on and close to the data.

What you'll do

• Own the data platform end to end. Design, build, and operate the warehouse, pipelines, and models that power internal analytics and customer-facing outputs, ingesting transaction events, provider statements, FX rates, contract terms, and product usage. Reliability, lineage, and documentation are yours.
• Make Bluefyn data-driven. Work with the founders to define the north star and supporting metrics, implement each one with a single unambiguous definition, and build the dashboards the company runs on. One metric, one definition, one source of truth.
• Prepare client-facing reports. Build the analyses customers see: provider cost breakdowns, benchmark comparisons, variance and savings summaries, and pilot results, every number traceable to its source.
• Own pilot data onboarding. Take messy customer inputs, provider exports, statements, transaction files, and contract terms, and map them into Bluefyn quickly and correctly during sales pilots and customer onboarding.
• Guard data quality. Build validation, reconciliation checks, anomaly detection, freshness monitoring, and alerting so failures are caught before anyone acts on bad data. Convert every incident into a permanent check.
• Support calculation validation. Work with Product, Engineering, and QA to independently verify FX, fee, billing, ledger, and balance calculations against reference data.
• Apply science where it earns its keep. Use statistics and modeling for fee and FX anomaly detection, benchmark methodology, forecasting, and evaluating AI outputs. Simple and explainable beats clever and opaque.
• Build the future function. Establish standards for modeling, testing, documentation, and access, then hire, mentor, and lead the data team as Bluefyn scales.

What we're looking for

• 5+ years of hands-on data experience. Spanning data engineering and analytics, with meaningful exposure to fintech, payments, billing, or other financial data where incorrect numbers create financial, customer, or operational risk. You may lean engineer, analyst, or scientist. What matters is covering the full path from raw data to a number someone acts on.
• Strong SQL and pipeline craft. Expert SQL, solid Python, and real production experience with data modeling, transformation frameworks such as dbt, orchestration, and a modern warehouse.
• Financial data fluency. You are comfortable with transactions, fees, FX, billing, balances, and reconciliation-style problems, and you never present a number you cannot trace.
• Metrics discipline. You can define a metric precisely, defend the definition, and implement it once so it stays consistent everywhere it appears.
• Customer-ready communication. You can turn analysis into a clear report or dashboard a CFO can act on, and calmly explain your method when an expert challenges it.
• An AI-native working style. You already run coding agents and other agentic tools for real work, understand guardrails, human approval, and common failure modes, and know how to validate every output before it reaches a decision or a customer.
• High ownership and startup range. Small inconsistencies bother you until you understand the cause, you communicate risk and reality clearly, and you are comfortable in a fast-moving stealth startup with broad ownership and limited process.

Nice to have

• Fintech domain depth. Experience with payments data: interchange, scheme fees, FX, provider statements, settlement files, ledgers, or billing systems.
• Modern stack experience. dbt, Airflow or Dagster, BigQuery, Snowflake, or Postgres at scale, and BI tools such as Metabase or Looker.
• Data science and AI evaluation depth. Anomaly detection, forecasting, or statistical inference in production, or datasets, eval harnesses, and monitoring for LLM and agent systems.
• Messy-data experience. You have onboarded data from inconsistent CSVs, statements, PDFs, and third-party exports and built tooling to make it repeatable.
• First-data-hire or early leadership experience. You have built a data platform from scratch, or mentored analysts and engineers and can grow into formal leadership.

Why this role

• A true founding seat. Define how the company measures itself and proves its value from the beginning, with a clear path to Data Lead as the company and team expand.
• Data is the product's credibility. The numbers you produce drive internal decisions and land directly in front of CFOs, and they are the reason customers can trust the platform. The same rigor serves both audiences.
• Small senior team. Work directly with the founders and engineers, with broad ownership and minimal bureaucracy.