Director, Data Engineering & Analytics

Relation Insurance leads the strategy, architecture, delivery, and continuous improvement of its enterprise data engineering and analytics capabilities. The Director owns the data platform and analytics function end to end, delivering trusted data solutions, advancing responsible AI and automation, and developing technical professionals.

Responsibilities:

• Owns Relation’s data engineering and analytics function end to end, with full responsibility and accountability for execution and outcomes
• Translates business priorities, as set by executive and business leadership, into the data and analytics roadmap, sequences the work within those priorities, and drives it to completion, owning the outcome regardless of team size
• Serves as the primary point of contact and accountable authority for executives, business leaders, auditors, and carrier partners on data, reporting, and analytics matters
• Provides executive-ready reporting on platform health, delivery progress, data quality, and data risk, and walks executives through the numbers in plain language
• Partners with IT, Information Security, Finance, Operations, and business leadership to obtain source access, implement controls, and drive adoption, while retaining accountability for data interpretation and outcomes
• Personally designs, builds, and maintains production data pipelines from core brokerage and enterprise systems
• Owns data processes end to end: understanding the business need, profiling source data, modeling, transforming, validating, delivering, and supporting the result in production
• Writes production-quality SQL, Python, and PySpark as a regular part of the job; troubleshoots failed loads, data discrepancies, and performance issues down to the root cause
• Builds and maintains curated, reusable datasets and data models for core domains (e.g., Client, Policy, Submission, Carrier, Producer, Revenue, Commission)
• Defines and standardizes KPI logic and Power BI semantic models (e.g., premium, revenue, retention, hit ratio, pipeline, cross-sell, service metrics) so the business works from one version of the truth
• Builds and delivers executive dashboards and self-service reporting, and works directly with business stakeholders to drive adoption
• Makes practical architecture decisions for the lakehouse platform (medallion bronze/silver/gold layers, ELT patterns, orchestration) with a focus on reliability, simplicity, and cost
• Directs AI coding agents as the function’s primary delivery capacity: decomposes initiatives into well-specified tasks, delegates implementation, testing, documentation, and source-data analysis to agents, reviews the output, and ships the result
• Builds AI-driven validation into every data process, including automated reconciliation, anomaly detection, and agent-based review of code and outputs, while remaining personally accountable for the accuracy of anything AI produces
• Sets and enforces the standards for secure, effective use of AI, and coaches any team members on working AI-first, including keeping sensitive client and employee data within approved tools and environments, maintaining human review of AI output before it reaches production or the business, and partnering with Information Security on AI governance
• Identifies and delivers opportunities to apply AI and automation to manual reporting, reconciliation, and data-entry work across the business
• Builds and operates production LLM-powered data workflows, such as document and data extraction from carrier and client documents, classification, and automated validation, with the same engineering rigor as any other pipeline
• Keeps the platform AI-maintainable: well-structured, documented, tested, and reproducible, so that any pipeline or report can be understood, modified, and recovered by an agent or another engineer without tribal knowledge
• Continuously evaluates new AI capabilities and adopts the ones that measurably improve speed, quality, or coverage. Leads, coaches, and develops data engineering and analytics professionals through hands-on work alongside the team, establishing clear expectations, accountability, technical standards, and opportunities for career growth. Reviews code and data models, whether produced by team members or AI agents, pairs on difficult problems, and raises the technical bar through practical coaching
• Manages intake of data requests, aligns prioritization with business leadership, sets realistic timelines, and keeps business partners informed
• Establishes lightweight engineering standards: Git-based workflows in GitHub, pull request review, CI/CD (GitHub Actions), testing, documentation, and production support, and applies them equally to the team’s work and to the output of AI agents
• Sets clear goals, provides ongoing feedback and performance management, and supports succession and development planning
• Defines and enforces data quality controls, testing, monitoring, and incident response (data SLAs)
• Maintains practical governance: documentation, data definitions, lineage, and certified datasets, and supplies the data-flow and system inventory that Information Security relies on for enterprise data mapping
• Ensures access controls and privacy-by-design for sensitive data (client PII, financial data), including retention and auditability aligned to company risk/compliance standards
• Performs other projects, duties, and tasks, as assigned

Requirements:

• Bachelor's degree in Computer Science, Information Systems, or a related field, or equivalent practical experience
• Minimum 10 years of progressively responsible hands-on experience in data engineering, analytics engineering, data architecture, data analytics or related technology roles
• Demonstrated ability to operate as the accountable owner of a data platform or analytics function, delivering outcomes against priorities set by the business
• Previous leadership experience, including responsibility for leading and developing technical teams, people leaders, projects, or complex cross-functional initiatives
• Currently hands-on: writes production SQL, Python, and PySpark as a regular part of the job, not only reviews it
• Demonstrated track record of owning data processes end to end and delivering measurable business outcomes
• Deep, daily, hands-on use of AI across the full engineering lifecycle, including agentic coding tools, LLM APIs, and automated validation, with concrete examples of delegating substantial multi-step work to AI agents, catching their mistakes, and owning the result
• Data modeling (dimensional + operational/normalized patterns), MDM concepts, and entity resolution across systems and acquired agencies
• SQL, Python, and Spark (PySpark)
• Lakehouse and medallion architecture, ELT, orchestration, and pipeline reliability patterns
• Data quality testing, reconciliation, observability, and production support
• Role-based access controls, secure data handling, and audit-ready processes
• Specifying, orchestrating, and reviewing the work of AI coding agents, including evaluation of AI-generated code, data, and analysis
• Exceptional written and verbal communication skills; comfortable presenting to and advising executives, and able to explain data, tradeoffs, and risk clearly to non-technical partners
• Highly organized, with the ability to manage multiple concurrent initiatives and competing executive priorities and to prioritize calmly in a fast-paced environment
• Detail-oriented while maintaining a strategic, enterprise-level perspective