IND Principal Data Engineer

On-siteSeniorData EngineerJust posted

✦Role summaryAI-generated

Lead cross-functional teams to design and operate a large-scale data ecosystem that integrates structured, semi-structured, and unstructured data for AI and agentic solutions. Drive the development of end-to-end data and AI pipelines, including extraction, chunking, embedding, and grounding strategies, to empower advanced analytics across the organization.

About this role

IND Director, Applied AI and Analytics - GCC145

We’re determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals – and to help others accomplish theirs, too. Join our team as we help shape the future.

Key Responsibilities

Lead teams to build and operate large and complex data ecosystem leveraging data domains, data products, cloud and modern technology stack

Lead teams responsible for Implementing Data and AI pipelines that bring together structured, semi-structured and unstructured data to support AI and Agentic solutions. This Includes pre-processing with extraction, chunking, embedding and grounding strategies to get the data ready.

Lead teams implementing real time data streaming pipelines using technologies such as Apache Kafka, AWS Kinesis, Spark streaming, or similar.

Stay up to date with industry advancements in GenAI and apply modern technologies and methodologies to our systems. This includes leading prototypes (POCs), conducting experiments, and recommending innovative tools and technologies to enhance data capabilities enabling business strategy.

Identify and Champion AI driven Data Engineering productivity improvements capabilities accelerating end-to-end data delivery lifecycle.

Lead teams designing and developing graph database solutions for complex data relationships supporting AI systems, this also includes developing and optimizing queries (e.g., Cyhper, SPARQL) to enable complex reasoning, relationship discovery, and contextual enrichment for AI agents.

Lead Implementation of best practices in reliability engineering, including redundancy, fault tolerance, and disaster recovery strategies.

Simplify architectures to increase velocity, quality, and reliability

Enable data-driven decisions that advance The Hartford’s strategic growth agenda

Champion a strong data culture across the enterprise

Own outcomes for programs/portfolios; establish delivery strategy, guardrails, and success metrics

Drive execution from initiation through release and stabilization; manage scope, estimates, schedules, risks/issues

Promote advanced Agile behaviors, coach teams toward self-organization and continuous improvement

Serve as a trusted point of contact for sponsors and leaders; proactively manage risks and dependencies

Mentor Scrum Masters/Data Engineers/Tech Leads; coordinate partner resources; uphold engineering and documentation standards

Align delivery with technical design; leverage knowledge of modern data stacks (AWS, Snowflake, ETL/ELT), CI/CD, and DevOps)

Support the development and implementation of project and portfolio strategy, roadmaps, and implementations

Operationalizing GitHub Copilot and Snowflake Copilot across multiple scrum teams with review standards and SDLC governance

Own portfolio‑level AI adoption metrics (hours saved per sprint, PR review effort, pipeline lead‑time, retrieval success rate); publish quarterly accelerator impact to EDS leadership.

Standardize Copilot review gates and agent guardrails; achieve ≥20% reduction in boilerplate coding time and ≥15% improvement in documentation completeness.

Required Skills & Experience:

7 to 8 years in technology leadership/delivery/program/project management with complex, multi-team initiatives

Overall 10+ years of data engineering experience including Data solutions, SQL and NoSQL, Snowflake, ETL/ELT tools, CICD, Bigdata, Cloud Technologies (AWS/Google/AZURE), Python/Spark, Datamesh, Datalake or Data Fabric.