[Hiring] Director of Technology, Enterprise Data Engineering @EIG Services Inc

Role Description

At EMPLOYERS, technology isn't just IT overhead — it's how we get injured workers back on their feet faster and help businesses stay protected. As Director of Technology, Enterprise Data Engineering, you'll lead the team building the modernized data foundation behind that mission. This role is accountable for workstreams that include:

• Running the business; keeping existing ingestion, orchestration, and streaming pipelines available and accurate.
• Modernization, retiring the legacy Data Lake and Curated Data Store onto the Databricks platform.
• Delivering ingestion, modeling, and curation for new and changing products.

You'll lead a team of data engineers through and beyond this modernization, working hands-on alongside them, while serving as the closest engineering partner to Enterprise Architecture on data modeling and platform design decisions. You are responsible for:

• Setting the technical direction for how EIG builds data pipelines going forward.
• Establishing the standards, patterns, and practices that keep the Databricks platform reliable, scalable, and audit-ready in a regulated industry.
• Championing responsible AI adoption in how the team works.

As EIG shifts toward a distributed, pod-based operating model, this role increasingly leads through platform, standards, and AI-augmented tooling rather than direct central control.

Qualifications

• 10+ years of experience in data engineering or a related specialized technology area, including 3+ years leading a team.
• Bachelor's degree in Computer Science, Information Systems, or related field, or equivalent experience.
• Insurance industry experience preferred.

Requirements

• Experience leading a legacy-to-cloud data platform migration.
• Experience managing vendor SaaS, as well as home-grown system data integrations.

Benefits

• Salary Range: $120,000 - $186,000 and a comprehensive benefits package.

Essential Duties and Responsibilities

Leadership & People Management

• Builds, coaches, and develops a team of data engineering professionals.
• Leads hiring and staffing decisions.
• Manages a mix of employees, contractors, and vendor-supplied resources.
• Coaches and holds accountable engineers embedded in cross-functional domain pods.
• Builds redundancy into critical skills and keeps documentation updated.

Business Responsibilities

• Partners with Enterprise Architecture to translate architectural direction into built pipelines.
• Partners with Analytics Operations, Enterprise Data, and Business Intelligence/Strategy.
• Drives strategic planning, sequencing, and trade-off decisions for data modeling.
• Serves as the internal technical counterpart to System Integration Partners.
• Maintains regular communication with Director peers and stakeholders.
• Enables autonomous development teams to build and operate Databricks pipelines.

Technical Responsibilities

• Owns data ingestion and the build of medallion architecture standards.
• Builds and owns the semantic data layer.
• Collaborates with Enterprise Architecture on data modeling standards.
• Sequences and delivers the domain-by-domain migration from legacy systems to Databricks.
• Sets the team's engineering standards for reproducible, version-controlled pipeline development.
• Ensures core data pipelines and platform infrastructure remain available and performant.
• Collaborates on the platform’s paved road.
• Leads troubleshooting of complex pipeline and data quality issues.
• Guides technical design sessions for secure and scalable data architecture.
• Researches and evaluates emerging platform capabilities.
• Maintains and enforces data engineering standards and documentation.

AI Collaboration Responsibilities

• Uses AI tools in daily work to improve efficiency and quality.
• Validates the accuracy of AI-generated outputs.
• Provides human oversight of AI agents within team workflows.
• Coaches team members on AI tool application.
• Champions AI and automation use cases within the team's domain.
• Sets standards for AI-driven development practices.

Governance & Risk

• Structures engineering output to meet data governance and quality certification requirements.
• Partners with Information Security to ensure compliance with policies and regulations.
• Supports audits and remediates vulnerabilities.
• Establishes lightweight AI governance practices.
• Defines and monitors KPIs and SLAs for data pipelines.
• Leverages automated guardrails for compliance.

Continuous Improvement

• Stays current on data platform capabilities and AI-assisted engineering tooling.
• Measures the effectiveness of AI adoption and adjusts practices.
• Takes an active role in cross-departmental projects.