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Remote Data Platform Engineer Jobs in Connecticut

Finalsite

Staff Engineer, Composer Platform

Glastonbury, CT · On-site +1

$180K - $250K/yr

... Staff Engineer, Composer Platform will provide technical leadership for Finalsite's core CMS ... LOCATION 100% Remote - Anywhere within the US RESPONSIBILITIES * Define and lead technical strategy ...

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Remote Data Platform Engineer information

See Connecticut salary details

$42.3K

$123.4K

$168.9K

How much do remote data platform engineer jobs pay per year?

As of Jul 18, 2026, the average yearly pay for remote data platform engineer in Connecticut is $123,397.00, according to ZipRecruiter salary data. Most workers in this role earn between $108,900.00 and $130,800.00 per year, depending on experience, location, and employer.

What is the difference between Remote Data Platform Engineer vs Data Engineer?

AspectRemote Data Platform EngineerData Engineer
CredentialsBachelor's in CS, Data Science, or related; experience with cloud platformsBachelor's in CS, Data Science, or related; experience with databases and ETL tools
Work EnvironmentRemote or hybrid, collaborating with data teams and cloud providersOn-site or remote, focusing on data pipelines and infrastructure
Industry UsageTech, finance, healthcare, and other data-driven sectorsSame industries, often overlapping roles
Search & Comparison IntentHigh overlap, often searched together due to similar skills

The Remote Data Platform Engineer focuses on building and maintaining cloud-based data platforms, ensuring scalability and performance. Data Engineers develop and manage data pipelines, databases, and ETL processes. While roles overlap in skills and industries, the Platform Engineer emphasizes cloud infrastructure, whereas Data Engineers concentrate on data processing and storage.

What are the key skills and qualifications needed to thrive as a Remote Data Platform Engineer, and why are they important?

To thrive as a Remote Data Platform Engineer, you need expertise in data engineering, database management, and cloud platforms, usually supported by a degree in computer science or a related field. Familiarity with tools such as SQL, Python, Spark, AWS, Azure, and certifications like AWS Certified Data Analytics are highly valued. Strong problem-solving, communication, and self-motivation skills help you excel in remote, collaborative environments. These skills and qualities ensure the efficient design, operation, and scaling of robust data platforms that drive business insights and operations.

What are the typical collaboration methods for a Remote Data Platform Engineer working with cross-functional teams?

As a Remote Data Platform Engineer, you will frequently collaborate with data scientists, analysts, and software engineers through virtual meetings, project management tools, and shared documentation platforms. Effective communication is essential, as much of the teamwork happens asynchronously using tools like Slack, Jira, and Confluence. You may participate in regular stand-ups, sprint planning, and code reviews to ensure alignment across the team. Building strong relationships remotely often involves proactive updates and clear documentation to keep all stakeholders informed and projects moving smoothly.

What is a Remote Data Platform Engineer?

A Remote Data Platform Engineer is a professional who designs, builds, and manages large-scale data infrastructure and platforms, while working remotely. They are responsible for ensuring data is efficiently collected, stored, processed, and made accessible for analysis, often using cloud technologies and big data tools. These engineers collaborate with data scientists, analysts, and other engineers to maintain data pipelines and optimize system performance. Their work enables organizations to leverage data for business insights, all while enjoying the flexibility of remote work.
What are popular job titles related to Remote Data Platform Engineer jobs in Connecticut? For Remote Data Platform Engineer jobs in Connecticut, the most frequently searched job titles are:
What job categories do people searching Remote Data Platform Engineer jobs in Connecticut look for? The top searched job categories for Remote Data Platform Engineer jobs in Connecticut are:
What cities in Connecticut are hiring for Remote Data Platform Engineer jobs? Cities in Connecticut with the most Remote Data Platform Engineer job openings:
Remote Data Platform Engineer jobs near you
AVP, Data Platform Engineering

AVP, Data Platform Engineering

The Hartford Financial Services Group, Inc.

Hartford, CT • On-site, Remote

$115K - $138K/yr

Full-time

Posted 8 days ago

The Hartford rating

8.8

Company rating: 8.8 out of 10

Based on 111 frontline employees who took The Breakroom Quiz

54th of 281 rated insurance

Job description

AVP IT Engineering - IE05AE
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.
As the AVP, Data Platform Engineering, you will provide strategic and technical leadership for enterprise data platforms, analytics capabilities, data integration services, AI-enabled data solutions, and Third-Party Data enablement across The Hartford.
This role is responsible for leading teams that build, modernize, and operate scalable, secure, and reliable data platforms that support analytics, business intelligence, AI capabilities, and enterprise consumption of internal and external data assets. The successful candidate will drive platform strategy, engineering excellence, operational effectiveness, technology transformation, and Third-Party Data capabilities while ensuring alignment to enterprise priorities and business objectives.
The ideal candidate combines strong engineering leadership, enterprise data platform expertise, and organizational transformation experience with the ability to influence technical and business stakeholders. This leader will play a critical role in advancing modern data capabilities, supporting enterprise AI initiatives, enabling enterprise adoption of Third-Party Data assets and services, and developing high-performing teams that deliver measurable business value.
Responsibilities
Engineering Leadership & Strategy
  • Define and execute a multi-year strategy for enterprise data platforms, analytics enablement, AI-ready data capabilities, data integration services, Third-Party Data capabilities, and platform modernization aligned to business and technology priorities.
  • Serve as the senior technical leader for enterprise data platforms, providing architecture guidance, engineering direction, and technology decision-making across data, analytics, AI-enabled capabilities, and external data ecosystems.
  • Partner with Architecture, Product, AI & Analytics, Cybersecurity, Data Governance, Procurement, Risk, Legal, and business leaders to define platform roadmaps, service offerings, adoption plans, and measurable outcomes.
  • Lead organizational transformation initiatives that improve engineering maturity, operational effectiveness, delivery speed, and team capabilities.
  • Build, mentor, and develop high-performing engineering leaders and teams through coaching, talent development, succession planning, and organizational design.
  • Foster a culture of innovation, accountability, technical excellence, collaboration, and continuous improvement.
  • Serve as a trusted advisor to senior technology and business leaders on platform modernization, AI enablement, Third-Party Data capabilities, emerging technologies, and enterprise data investments.

Data Platform Engineering
  • Lead engineering teams responsible for enterprise data platforms and services, including Snowflake, Spark, Google BigQuery, Dataproc, Dataflow, Informatica IDMC, and related cloud-native data engineering capabilities.
  • Drive modernization of enterprise data platform capabilities through cloud adoption, platform rationalization, legacy migration, automation, and scalable engineering practices.
  • Establish engineering standards and best practices for platform architecture, data ingestion, orchestration, transformation, observability, reliability, performance optimization, security, automation, and cost management.
  • Improve engineering productivity through platform standardization, self-service capabilities, reusable engineering patterns, CI/CD adoption, infrastructure-as-code practices, and modern software engineering approaches.
  • Ensure enterprise data platforms are designed and operated for scalability, resilience, security, compliance, and operational excellence.
  • Lead the operationalization of new and evolving platform capabilities and services across the enterprise data ecosystem.

Analytics & Business Intelligence
  • Enable enterprise analytics and business intelligence capabilities through platforms such as Tableau and ThoughtSpot, emphasizing trusted datasets, reusable data products, governed data access, semantic modeling, and self-service analytics.
  • Partner with analytics and business teams to deliver scalable, trusted, and business-aligned insights.
  • Advance next-generation analytics experiences including conversational analytics, Chat with Data, AI-assisted insight generation, embedded intelligence, and Agentic Analytics capabilities.
  • Drive modernization of analytics capabilities to improve data accessibility, business adoption, governance, performance, and user experience.

AI-Ready Data Foundations
  • Lead the engineering strategy and platform capabilities that support AI-ready enterprise data platforms and services.
  • Build and scale foundational capabilities supporting semantic layers, ontology frameworks, knowledge graphs, contextual metadata, and trusted business definitions.
  • Partner with AI and analytics leaders to ensure enterprise platforms support emerging AI use cases and future AI initiatives.
  • Support integration of technologies and capabilities such as Snowflake Cortex, Gemini Enterprise integrations with BigQuery, vector search technologies, Retrieval-Augmented Generation (RAG) architectures, and AI/ML platform interoperability.
  • Ensure AI-enabling data capabilities are governed, scalable, secure, reusable, and aligned with enterprise architecture and governance standards.

Data Integration & Third-Party Data Enablement
  • Lead teams responsible for enterprise data integration, ingestion, API enablement, Third-Party Data services, and data movement capabilities across internal and external data ecosystems.
  • Serve as the executive leader for Third-Party Data platform capabilities, partnering with business stakeholders, data consumers, and strategic vendors to enable enterprise access to external data assets and services.
  • Establish and evolve modern data acquisition patterns, onboarding frameworks, integration standards, and underlying technologies that support scalable and secure Third-Party Data consumption across the enterprise.
  • Drive enterprise capabilities supporting external data onboarding, ingestion, governance, compliance, lineage, quality, metadata management, and operational support.
  • Partner with business leaders and strategic vendors to prioritize Third-Party Data initiatives and ensure platform capabilities align with enterprise data needs and business objectives.
  • Lead collaboration across technology, legal, risk, procurement, governance, and business stakeholders to ensure Third-Party Data solutions meet enterprise standards for compliance, security, and operational excellence.
  • Champion adoption of modern Third-Party Data capabilities, frameworks, and reusable integration patterns across the enterprise.
  • Translate stakeholder requirements into actionable platform, integration, and data engineering priorities.

Global & Matrixed Leadership
  • Lead distributed engineering teams across multiple geographies and delivery models, including global partners and support organizations.
  • Influence teams and stakeholders across organizational boundaries, including groups that do not directly report into the organization.
  • Partner effectively with global engineering, support, and delivery organizations to drive alignment, accountability, and execution.
  • Establish operating models and delivery practices that support collaboration across employees, contractors, and global teams.

Required Qualifications
  • 12+ years of experience in data platform engineering, data architecture, cloud data platforms, analytics technologies, infrastructure engineering, or related disciplines, with a proven track record of leadership in complex enterprise environments.
  • Deep engineering leadership experience building, operating, and modernizing enterprise-scale data platforms, including Snowflake, Spark, Google BigQuery, Dataproc, Dataflow, Informatica IDMC, and comparable cloud-native technologies.
  • Strong understanding of data platform engineering practices, including platform architecture, ingestion, orchestration, transformation, observability, reliability, automation, performance tuning, cost management, security, and operational support.
  • Hands-on engineering background with the technical depth to guide architecture decisions, evaluate engineering tradeoffs, influence technology strategy, and provide credible leadership to architects and engineers.
  • Demonstrated success leading platform modernization, cloud transformation, engineering maturity improvements, and organizational change initiatives.
  • Experience leading enterprise Third-Party Data capabilities, including external data acquisition, vendor-enabled data services, onboarding frameworks, governance, compliance, and operational management.
  • Demonstrated success partnering with business stakeholders, strategic vendors, and cross-functional teams to deliver solutions leveraging external data assets and services.
  • Strong understanding of Third-Party Data lifecycle management, including acquisition, integration, governance, quality, lineage, compliance, risk management, and enterprise consumption patterns.
  • Experience with modern analytics and business intelligence platforms such as Tableau and ThoughtSpot, including self-service analytics, semantic modeling, dashboard modernization, and business user enablement.
  • Strong understanding of AI-enabled data ecosystems, including Snowflake Cortex, Gemini Enterprise integrations with BigQuery, conversational analytics, Chat with Data, Agentic Analytics, vector search technologies, AI/ML integration patterns, and Retrieval-Augmented Generation (RAG) architectures.
  • Experience supporting or implementing semantic layers, ontology-driven solutions, knowledge graphs, contextual metadata, metrics layers, and AI-ready enterprise knowledge models.
  • Exceptional strategic thinking, systems thinking, and problem-solving capabilities with the ability to balance near-term execution and long-term platform strategy.
  • Strong executive communication, presentation, and storytelling skills with the ability to explain complex technical concepts to both technical and non-technical audiences.
  • Proven ability to build, mentor, and develop high-performing teams while leading through organizational change and transformation.
  • Experience influencing stakeholders and driving results in highly matrixed organizations.

Preferred Qualifications
  • Experience with Google Cloud Platform and cloud-native data engineering ecosystems.
  • Experience leading Snowflake modernization, migration, optimization, or platform strategy initiatives.
  • Experience supporting semantic layer, ontology, knowledge graph, metadata, or AI-ready data platform initiatives.
  • Experience building or modernizing enterprise Third-Party Data platforms, acquisition frameworks, vendor data ecosystems, and external data enablement capabilities.
  • Experience with enterprise data integration, API enablement, Informatica, master data management, and large-scale data ecosystems.
  • Experience leading distributed or global engineering teams.
  • Strong knowledge of data governance, cybersecurity, privacy, compliance, data quality, lineage, metadata management, and risk management practices.
  • Experience within insurance, financial services, or other highly regulated industries.
  • Bachelor's degree in Computer Science, Data Science, Information Systems, Engineering, Business Administration, or a related quantitative field.

Location Requirements:
  • This role can have a Hybrid or Remote work arrangement. Candidates who live near our Hartford, CT or Charlotte offices will have the expectation of working in an office 3 days a week (Tuesday through Thursday). Candidates who do not live near an office should maintain their current work arrangement with the expectation of coming into the office as business needs arise.

Compensation
The listed annualized base pay range is primarily based on analysis of similar positions in the external market. Actual base pay could vary and may be above or below the listed range based on factors including but not limited to performance, proficiency and demonstration of competencies required for the role. The base pay is just one component of The Hartford's total compensation package for employees. Other rewards may include short-term or annual bonuses, long-term incentives, and on-the-spot recognition. The annualized base pay range for this role is:
$177,600 - $266,400
Equal Opportunity Employer/Sex/Race/Color/Veterans/Disability/Sexual Orientation/Gender Identity or Expression/Religion/Age
About Us | Our Culture | What It's Like to Work Here | Perks & Benefits

What The Hartford employees say

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Hartford logo

About Hartford

Sourced by ZipRecruiter

Hartford Financial Services Group, widely recognized as The Hartford, is a renowned company based in Hartford, CT, US. Established in 1810, it has evolved into an industry leader in the insurance and financial services sector, proudly serving more than one million businesses in the US. The Hartford is committed to offering a gamut of insurance products that include homeowners, automobile, and business insurance as well as employee benefits and mutual funds. The company’s core values revolve around customer-focused innovations, diversity and inclusion, and ethical dealings that have earned them a customer-centric reputation. This shapes their mission which revolves around aiding their clients to overcome unforeseen obstacles and enhancing their wealth over time. Among the company's noted accomplishments is being consistently listed among the World's Most Ethical Companies, a testament to their unwavering commitment towards responsible business practices.

Industry

Finance and insurance

Company size

10,000+ Employees

Headquarters location

Hartford, CT, US

Year founded

1810

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