Director, Data Engineering & Analytics

relationinsurance; United States; In loco; USD 180.000 - 260.000; Tempo pieno

Descrizione del lavoro

Relation Insurance seeks a Director of Data Engineering and Analytics to own the data platform and analytics function end to end. You will lead hands-on technical teams, shape the data roadmap, and partner with executives to translate priorities into scalable, trusted data solutions.

The role includes building production pipelines, governance, and AI-enabled data workflows. The ideal candidate will drive architecture, reliability, and cost efficiency while mentoring engineers and ensuring

Mansioni

• Owns Relation's data engineering and analytics function end to end with full accountability for execution and outcomes.
• Translates business priorities into the data and analytics roadmap and drives completion, owning the outcome.
• Primary point of contact 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 in plain language.
• Partners with IT, Information Security, Finance, Operations, and business leadership to obtain source access, implement controls, and drive adoption.
• Designs, builds, and maintains production data pipelines from core brokerage and enterprise systems.
• Writes production-quality SQL, Python, and PySpark; troubleshoots loads, discrepancies, and performance issues.
• Builds and maintains curated datasets and data models for core domains (Client, Policy, Submission, Carrier, Producer, Revenue, Commission).
• Defines and standardises KPI logic and Power BI models so the business uses one version of the truth.
• Builds dashboards and self-service reporting and drives adoption with stakeholders.
• Makes architecture decisions for the lakehouse platform with focus on reliability, simplicity, and cost.
• Directs AI coding agents as the primary delivery capacity and ensures outputs are validated.
• Sets AI governance standards and coaches team on AI-first work with secure handling of sensitive data.
• Identifies opportunities to apply AI and automation to reporting and data-entry tasks across the business.

Descrizione del lavoro

WHO WE ARE

Relation Insurance is a leading, innovative company with a strong commitment to excellence and a passion for delivering cutting-edge solutions to our clients. As a key player in the insurance market, we pride ourselves on our dynamic culture, collaborative environment, and continuous drive for success. With a rich history and a bright future ahead, we are looking for exceptional individuals to join our team and contribute to our ongoing growth and success.

WHAT WE'RE LOOKING FOR

The Director, Data Engineering and Analytics leads the strategy, architecture, delivery, and continuous improvement of Relation's enterprise data engineering and analytics capabilities. The individual in this role owns the data platform and analytics function end to end, ensuring scalable, reliable, secure, and trusted data solutions that support business priorities and measurable outcomes. The Director provides hands‑on technical leadership, advances the responsible use of automation and proprietary AI, and leads and develops data engineering and analytics professionals. The Director partners with executive, business, and technology leaders to translate priorities into a practical data roadmap and drive execution. The individual in this role establishes standards for data architecture, integration, quality, governance, security, reporting, and analytics while remaining sufficiently hands‑on to guide complex technical work, resolve issues, and clearly communicate data, tradeoffs, and risk to non‑technical stakeholders.

A GLIMPSE INTO YOUR DAY

Ownership and Accountability

• 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 standardises 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, m