Senior Data Engineer

• Domain experience in payments, FinTech, or financial-services data - familiarity with concepts such as interchange, scheme/network fees, take rate, chargebacks, authorization rates, or surcharging.
• Experience integrating payment-processor data (e.g. Worldpay, Adyen, Stripe) and normalizing heterogeneous processor data into a single model.
• Experience supporting BI/analytics teams and self-service reporting tools (e.g. Power BI, Tableau, Looker, Sigma).
• Exposure to analytics engineering or hands-on work with forecasting / cohort-based analytical models.
• Data contracts and governance practices (dbt exposures, contracts/tests, semantic layers).

We are looking for a Senior Data Engineer to lead the build of an automated payments reporting and analytics platform for a client in the payments space. Today, revenue reporting for this business depends on manual journal entries and manual assembly of results - the general ledger and BI reporting do not reconcile without human intervention, and reporting is too aggregated to explain performance.

Your mission is to replace that with a consistent, automated data flow: daily pipelines that land processor, CRM, and ERP data into a cloud data warehouse, post automatically to the general ledger, and feed a reporting layer granular enough to decompose any variance to budget down to its root cause. This is a high-ownership role with direct visibility to finance and business stakeholders, where the quality of your data models directly determines whether the business can trust its numbers.

• Design and build automated, reliable ELT pipelines that integrate data from multiple source systems (payment processors, CRM, and ERP) into a cloud data warehouse on a daily cadence.
• Automate financial reporting to the general ledger that reconciles with the reporting layer by design, replacing manual, error-prone processes.
• Build and maintain a data-mapping layer that resolves customer identity across systems and rolls transactions up to a single customer key.
• Model clean, well-documented data marts that make business performance explainable and support drill-down and variance analysis.
• Deliver the data models that power a KPI and forecasting dashboard for finance and business stakeholders.
• Ensure pipelines are robust and auditable with monitoring, data-quality checks, and alerting so issues are caught before reporting is consumed.
• Collaborate with BI, finance, and integration partners to turn business requirements into reliable data products and define phased delivery.

Core: Snowflake, dbt, SQL, Python, a modern orchestrator (Airflow / Dagster / Prefect), Git + CI/CD.

Source systems in scope: multiple payment processors and a core payments API, a CRM (customer & pricing attributes), and an ERP/general ledger. A BI tool renders the end-user dashboards on top of the modeled data.

• 5+ years of professional experience in data engineering, with a track record of owning production data pipelines end to end.
• Expert-level SQL - complex analytical queries, window functions, query optimization, and dimensional / star-schema data modeling.
• Hands-on experience on a modern cloud data warehouse, ideally Snowflake (or comparable: BigQuery, Redshift, Databricks).
• Strong experience with dbt (or an equivalent ELT transformation framework) for transformation and building tested, documented, version-controlled data models.
• Proven experience building and scheduling ELT/ETL pipelines using a workflow orchestrator (e.g. Airflow, Dagster, Prefect) and designing idempotent, re-runnable, incremental loads.
• Experience implementing data-quality testing, monitoring, and alerting as first-class parts of a pipeline.
• Strong communication skills with the ability to work directly with finance/business stakeholders, clarify requirements, and explain data-model decisions clearly (English, written and spoken).