Senior Data Engineer

At Accelerant, better risk decisions come from better data. We're building a greenfield data enrichment capability that combines third-party datasets, internal products, AI-generated insights, and platform services into a trusted source of truth for underwriting, claims, exposure management, and data science.

As a Senior Data Engineer, you'll own the journey from evaluating new data vendors through to production-ready pipelines, matching logic, APIs, and enrichment services. This is a highly visible role where the quality of your work directly impacts business-critical risk decisions.

• Evaluate and onboard new data vendors, validating data quality, coverage, match rates, and operational fit.
• Build and operate scalable ingestion pipelines for APIs and bulk file feeds.
• Design ELT workflows using technologies such as Python, SQL, dbt, Snowflake (or equivalent).
• Develop entity resolution and record-linkage capabilities that connect messy external datasets to internal policy and claims data.
• Create APIs and services that make enriched data available across underwriting, analytics, data science, and platform teams.
• Implement data governance controls including lineage, source attribution, permissions, and usage restrictions.
• Own geocoding, address standardisation, monitoring, alerting, and operational support.
• Build confidence-scoring and multi-source data resolution frameworks to improve data quality and trust.
• Partner closely with Analytics Engineering, Data Science, Actuarial, and external vendors to deliver valuable data products.

• Proven experience building and operating production data pipelines using both API and file-based integrations.
• Strong experience with SQL and Python and/or Type Script.
• Experience with dbt, Snowflake, or modern cloud data platforms.
• Practical expertise in entity matching, record linkage, and data quality management.
• Experience building and supporting production APIs and services.
• Strong understanding of metadata, lineage, governance, permissions, and data usage controls.
• Comfortable taking ownership of ambiguous problems and turning them into reliable data products.
• Experience using AI-assisted development practices or working with LLM-generated data.