Director, Data Engineering & Analytics

Relation Insurance Services

6d • $178,000 - $294,000

About The Position

The Director, Data Engineering and Analytics leads the strategy, architecture, delivery, and continuous improvement of Relation’s enterprise data engineering and analytics capabilities. This role owns the data platform and analytics function end to end, delivering scalable, reliable, secure, and trusted solutions that support business priorities and measurable outcomes. The Director provides hands-on technical leadership, advances responsible automation and proprietary AI use, and leads and develops data engineering and analytics professionals.

The Director works with executive, business, and technology leaders to turn priorities into a practical data roadmap and drive its execution. The role sets standards for data architecture, integration, quality, governance, security, reporting, and analytics, while remaining hands-on in complex technical work and communicating data, tradeoffs, and risks to non-technical stakeholders.

The position combines production data engineering and analytics with technical and people leadership. It includes building and supporting data pipelines, curated datasets, data models, KPI definitions, Power BI semantic models, dashboards, and self-service reporting. It also includes directing AI coding agents, building AI-driven validation and LLM-powered workflows, setting standards for secure AI use, and maintaining production-quality engineering practices and governance.

Requirements

• Bachelor’s degree in Computer Science, Information Systems, or a related field, or equivalent practical experience.
• At least 10 years of progressively responsible hands-on experience in data engineering, analytics engineering, data architecture, data analytics, or related technology roles.
• Demonstrated ability to own a data platform or analytics function and deliver outcomes against business priorities.
• Leadership experience developing technical teams, people leaders, projects, or complex cross-functional initiatives.
• Currently hands-on writing production SQL, Python, and PySpark as a regular part of the job.
• Track record of owning data processes end to end and delivering measurable business outcomes.
• Deep, daily hands-on use of AI across the engineering lifecycle, including agentic coding tools, LLM APIs, and automated validation, with examples of delegating substantial multi-step work to AI agents, identifying errors, and owning results.
• Expertise in dimensional and operational/normalized data modeling, MDM concepts, and entity resolution across systems and acquired agencies.
• Expertise in SQL, Python, and Spark (PySpark).
• Expertise in lakehouse and medallion architecture, ELT, orchestration, and pipeline reliability patterns.
• Expertise in data-quality testing, reconciliation, observability, and production support.
• Expertise in role-based access controls, secure data handling, and audit-ready processes.
• Ability to specify, orchestrate, and review AI coding agents’ work, including evaluating AI-generated code, data, and analysis.
• Exceptional written and verbal communication skills, including presenting to and advising executives and explaining data, tradeoffs, and risks to non-technical partners.
• Highly organized, able to manage concurrent initiatives and competing executive priorities, and able to prioritize calmly in a fast-paced environment.
• Detail-oriented while maintaining a strategic, enterprise-level perspective.

Responsibilities

• Own the data engineering and analytics function end to end, with accountability for execution and outcomes.
• Translate business and executive priorities into a data and analytics roadmap, sequence work, and drive initiatives to completion.
• Serve as the primary accountable contact for executives, business leaders, auditors, and carrier partners on data, reporting, and analytics matters.
• Provide executive-ready reporting on platform health, delivery progress, data quality, and data risk.
• Partner with IT, Information Security, Finance, Operations, and business leaders to obtain source access, implement controls, and drive adoption.
• Personally design, build, and maintain production data pipelines from core brokerage and enterprise systems.
• Own data processes end to end, including profiling source data, modeling, transforming, validating, delivering, and supporting production results.
• Write and troubleshoot production SQL, Python, and PySpark, resolving data-load failures, discrepancies, and performance issues.
• Build and maintain curated, reusable datasets and data models for core business domains.
• Define and standardize KPI logic and Power BI semantic models, and build executive dashboards and self-service reporting.
• Make practical lakehouse architecture decisions, including medallion layers, ELT patterns, and orchestration.
• Direct AI coding agents by breaking initiatives into tasks, delegating implementation and related work, reviewing output, and delivering results.
• Build AI-driven validation into data processes, including automated reconciliation, anomaly detection, and agent-based review, while retaining accountability for accuracy.
• Set standards for secure and effective AI use, coach team members on AI-first practices, and partner with Information Security on AI governance.
• Identify and deliver opportunities to use AI and automation for manual reporting, reconciliation, and data-entry work.
• Build and operate production LLM-powered data workflows, including document and data extraction, classification, and automated validation.
• Keep the platform structured, documented, tested, and reproducible so pipelines and reports can be understood and maintained by others or AI agents.
• Evaluate new AI capabilities and adopt those that measurably improve speed, quality, or coverage.
• Lead, coach, and develop data engineering and analytics professionals; review code and data models, pair on difficult problems, and establish technical expectations and growth opportunities.
• Manage data-request intake, align priorities with business leadership, set realistic timelines, and communicate with business partners.
• Establish engineering standards for GitHub workflows, pull-request review, GitHub Actions CI/CD, testing, documentation, and production support.
• Set goals, provide ongoing feedback and performance management, and support succession and development planning.
• Define and enforce data-quality controls, testing, monitoring, and incident response, including data SLAs.
• Maintain governance through documentation, data definitions, lineage, and certified datasets, and provide data-flow and system inventories for enterprise data mapping.
• Ensure access controls and privacy-by-design for sensitive client and financial data, including retention and auditability aligned with company risk and compliance standards.
• Perform other projects, duties, and tasks as assigned.

Benefits

• Competitive pay.
• Family health and wellness programs.
• 401K.
• Employee assistance programs.
• Paid time off.
• Paid holidays.
• Career advancement and development opportunities.
• Potential eligibility for a discretionary annual incentive program, subject to program rules and applicable performance factors.

What This Job Offers

• Job Type: Full-time
• Career Level: Director