AVP, Data Platform Engineering

The Hartford

Confirmed live yesterday High trust
Remote Hybrid

Quick summary

Work type
Remote
Location
Hartford, CTCharlotte, NC
Salary
$177,600–$266,400 / yr
Posted
63 days ago
Freshness
Confirmed live yesterday

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $194k
This role $222k
$125k most similar roles pay here $282k

This role pays more than 73% of similar roles. Most pay $161,812–$225,625 — the shaded band above. At the midpoint, this role pays about $222k versus about $194k for comparable roles.

Based on 240 similar postings.

Employer

About The Hartford

The Hartford is a leading provider of property and casualty insurance, group benefits, and mutual funds, serving businesses and individuals across the United States. Industry: Insurance & Financial Services

The Hartford currently has 45 open roles on FindRole.

Listed pay typically runs $127,600–$191,400 across 37 roles with salary data.

Most-posted roles

View all roles at The Hartford

At a glance

TL;DR · AVP, Data Platform Engineering

As the AVP, Data Platform Engineering, you will provide strategic and technical leadership for enterprise data platforms, analytics capabilities, and AI-enabled data solutions. You will lead engineering teams to build, modernize, and operate scalable, secure, and reliable systems that support business intelligence and the consumption of internal and external data assets. Your daily work involves driving platform strategy, infrastructure-as-code practices, and automated CI/CD pipelines while managing third-party data integration and governance. The role requires expertise in Snowflake, Spark, Google BigQuery, Dataproc, Dataflow, Informatica IDMC, Tableau, and ThoughtSpot. You will also develop AI-ready foundations using technologies like Snowflake Cortex, Gemini Enterprise integrations with BigQuery, vector search, and Retrieval-Augmented Generation architectures. This position solves the challenge of transforming legacy systems into modern, high-performing data ecosystems to enable advanced analytics and automated insights across the enterprise.

What you'll do

  • Define and execute a multi-year strategy for enterprise data platforms, AI-ready capabilities, and third-party data integration.
  • Lead engineering teams in building and modernizing scalable data platforms using technologies like Snowflake, Spark, and Google BigQuery.
  • Establish engineering standards for data ingestion, orchestration, transformation, security, and cost management.
  • Develop infrastructure to support advanced analytics, including conversational AI, semantic layers, and knowledge graphs.
  • Manage the acquisition and integration of third-party data assets while ensuring compliance and governance.
  • Drive organizational transformation to improve engineering maturity, delivery speed, and operational effectiveness.
  • Mentor and develop high-performing engineering teams across global and matrixed environments.

What we're looking for

  • 12+ years of experience in data platform engineering, architecture, cloud platforms, or related disciplines.
  • Experience building and modernizing enterprise-scale platforms using Snowflake, Spark, Google BigQuery, Dataproc, Dataflow, and Informatica IDMC.
  • Expertise in data platform engineering practices including ingestion, orchestration, transformation, observability, reliability, and cost management.
  • Proven experience leading third-party data capabilities, including external data acquisition, vendor management, and governance.
  • Experience with modern analytics platforms like Tableau and ThoughtSpot to enable self-service analytics and semantic modeling.
  • Knowledge of AI-enabled data ecosystems, including Snowflake Cortex, Gemini integrations, vector search, and RAG architectures.
  • Ability to lead distributed teams in a matrixed organization while mentoring and developing high-performing engineering leaders.
  • Bachelor's degree in Computer Science, Data Science, Information Systems, Engineering, Business Administration, or a related quantitative field.

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