Director, Data Engineering – Analytics

Relation Insurance Services

. Lead the strategy, architecture, delivery, and continuous improvement of Relation’s enterprise data engineering and analytics capabilities .

Posted 10/16/2026full-timeRemote • United StatesLead💰 $178,000 - $294,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in data engineering and analytics, with a strong focus on SQL, Python, and PySpark for building and maintaining data pipelines. Proven ability to lead technical teams, manage data processes end to end, and deliver measurable business outcomes while ensuring data quality and security.

Highest-signal resume keywords

Data EngineeringSQLPythonAI IntegrationData Quality Testing

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data ArchitectureAnalytics EngineeringData ModelingELT PatternsPipeline ReliabilityRole-Based Access ControlsData GovernanceKPI LogicPower BIAI Coding Tools

Soft Skills

LeadershipCommunicationOrganizational SkillsDetail-OrientedStrategic Thinking

Tools & Technologies

GitHubLLM APIsData PipelinesProduction SupportSelf-Service Reporting

Industry Keywords

Data QualityData RiskAudit-Ready ProcessesData DiscrepanciesBusiness Priorities

Tech Stack

Tools & technologies

PySparkPythonSparkSQL

About the role

Key responsibilities & impact

Lead the strategy, architecture, delivery, and continuous improvement of Relation’s enterprise data engineering and analytics capabilities
Own the data platform and analytics function end to end, including execution and outcomes
Translate executive and business priorities into a practical data and analytics roadmap and drive execution
Serve as the accountable authority 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 leadership to obtain source access, implement controls, and drive adoption
Design, build, and maintain production data pipelines from core brokerage and enterprise systems
Own data processes from business need through profiling, modeling, transformation, validation, delivery, and production support
Troubleshoot failed loads, data discrepancies, and performance issues to root cause
Build curated reusable datasets and data models for core business domains
Define KPI logic and Power BI semantic models
Build executive dashboards and self-service reporting and drive stakeholder adoption
Make architecture decisions for the lakehouse platform, medallion layers, ELT patterns, and orchestration
Direct AI coding agents by decomposing initiatives, delegating work, reviewing outputs, and shipping results
Build AI-driven validation, reconciliation, anomaly detection, and agent-based code/output review into data processes
Establish and enforce standards for secure and effective AI use and partner with Information Security on AI governance
Identify and deliver AI and automation opportunities for reporting, reconciliation, and data entry
Build and operate production LLM-powered data workflows for document and data extraction, classification, and validation
Keep the platform structured, documented, tested, and reproducible
Evaluate and adopt AI capabilities that improve speed, quality, or coverage
Lead, coach, and develop data engineering and analytics professionals
Review code and data models, pair on difficult problems, and raise technical standards
Manage data request intake, prioritization, timelines, and stakeholder communication
Establish GitHub workflows, pull request review, GitHub Actions CI/CD, testing, documentation, and production support standards
Set goals, provide feedback and performance management, and support succession and development planning
Define and enforce data quality controls, testing, monitoring, incident response, governance, lineage, and certified datasets
Ensure access controls and privacy-by-design for sensitive client and financial data
Perform other assigned projects, duties, and tasks

Requirements

What you’ll need

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
Experience owning a data platform or analytics function and delivering outcomes against business priorities
Previous leadership experience leading and developing technical teams, people leaders, projects, or complex cross-functional initiatives
Current hands-on production SQL, Python, and PySpark experience
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
Expertise in 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
Experience specifying, orchestrating, and reviewing AI coding agents and evaluating AI-generated code, data, and analysis
Exceptional written and verbal communication skills, including presenting to and advising executives
Ability to explain data, tradeoffs, and risk clearly to non-technical partners
Highly organized, able to manage multiple concurrent initiatives and competing executive priorities
Detail-oriented with a strategic, enterprise-level perspective

Benefits

Comp & perks

Competitive pay
Family health and wellness programs
401K
Employee assistance programs
Paid time off
Paid holidays
Career advancement and development opportunities
Discretionary annual incentive program, subject to program rules and individual and organizational performance