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Remote Data Engineering Jobs in Connecticut (NOW HIRING)

The Hartford

Data & AI Delivery Lead

Hartford, CT · On-site +1

$156K - $234K/yr

Dir Data Engineering - GE06AE We're determined to make a difference and are proud to be an ... Hybrid / Or Remote This role can have a Hybrid or Remote work schedule. Candidates who live near ...

The Hartford

Director of Data Science

Hartford, CT · On-site +1

$153K - $229K/yr

Partner with Actuarial, Data Engineering, and other modeling organization teams to connect modeling ... Candidates who do not live near an office may be considered for a remote work arrangement with ...

Cigna

Data Science Director - Remote

Bloomfield, CT · On-site +1

As the Data Science Director for Pricing & Underwriting, you will lead high-impact teams that build ... Provide technical leadership across machine learning, statistical modeling, feature engineering ...

PROLIM Global Corporation

Dataiku Engineer

Stamford, CT · On-site +1

Stamford, CT/Remote Job Type: Long Term Contract Mandatory Skill: Dataiku Engineering experience ... Provide strategic guidance on data wrangling, workflow orchestration, and platform integration ...

Tiger Analytics Inc.

GenAI Data Engineer

Hartford, CT · On-site +1

$115K - $138K/yr

Our consultants bring deep expertise in Data Science, Machine Learning and AI. We are the trusted ... engineering flow to transform unstructured blobs into structured insights. * Tune complex SQL ...

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All JobsData Engineering JobsRemote Data Engineering JobsRemote Data Engineering Jobs in Connecticut

Remote Data Engineering information

See Connecticut salary details

$42.3K

$123.4K

$168.9K

How much do remote data engineering jobs pay per year?

As of Jul 18, 2026, the average yearly pay for remote data engineering 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.

Can I work remotely as a data engineer?

Yes, remote data engineering roles are common, allowing professionals to work from various locations. These jobs often require skills in cloud platforms, programming, and data pipeline tools, and may involve collaboration through online communication tools.

How do remote data engineers typically collaborate with other team members across different time zones?

Remote data engineers often work with distributed teams, which requires strong communication and organization skills. They collaborate using tools like Slack, Zoom, and project management platforms to stay aligned on data pipeline development, troubleshooting, and deployment. Regular stand-ups, asynchronous documentation, and clear communication of progress are essential for ensuring everyone is on the same page, regardless of location. Flexibility in working hours and proactive scheduling of meetings help facilitate effective collaboration and project delivery.

What is remote data engineering?

Remote data engineering involves designing, building, and maintaining data systems and pipelines while working from a location outside of a traditional office. Remote data engineers use tools to collect, process, and store large sets of data, making it accessible for analysis and business decision-making. They collaborate with teams virtually, often using cloud-based technologies, to ensure that data infrastructure is reliable, scalable, and secure. This role requires strong technical skills in programming, databases, and data architecture, as well as the ability to communicate effectively in a distributed work environment.

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

To thrive as a Remote Data Engineer, you need strong programming skills (such as Python, Java, or Scala), experience with data modeling, ETL processes, and a solid understanding of database systems, often supported by a degree in computer science or a related field. Proficiency with big data tools like Apache Spark, Hadoop, cloud platforms (AWS, Azure, GCP), and certifications in these technologies is highly valued. Excellent problem-solving abilities, self-motivation, and clear communication are crucial soft skills for remote collaboration and project delivery. These competencies ensure effective data pipeline development, reliable data management, and seamless teamwork across distributed environments.

What engineer makes $500,000 a year?

Senior data engineers with extensive experience, advanced skills in cloud platforms, and expertise in big data tools can earn salaries approaching or exceeding $500,000 annually, especially in high-cost-of-living areas or within large tech companies. Such compensation often includes bonuses, stock options, and other incentives.

How to make $1000 a week remote?

Remote data engineers can earn $1000 or more per week by working on high-demand projects, leveraging specialized skills in data pipelines, cloud platforms, and programming languages like Python or SQL. Building a strong portfolio, obtaining relevant certifications, and working with multiple clients or on freelance platforms can help increase weekly income. Consistent remote work and advanced expertise are key to reaching this earning level.

Is AI replacing data engineers?

AI is automating certain tasks within data engineering, such as data cleaning and pipeline management, but it does not replace the need for data engineers. Data engineers are essential for designing, building, and maintaining complex data systems, and their expertise in tools like SQL, Python, and cloud platforms remains critical for managing data workflows effectively.

What is the difference between Remote Data Engineering vs Remote Data Analyst?

AspectRemote Data EngineeringRemote Data Analyst
Required CredentialsBachelor's in CS, Data Science, or related field; experience with SQL, Python, cloud platformsBachelor's in Statistics, Data Science, or related; proficiency in Excel, SQL, visualization tools
Work EnvironmentBuilds data pipelines, manages databases, works with cloud infrastructureAnalyzes data sets, creates reports, visualizes data insights
Employer & Industry UsageTech companies, finance, healthcare, e-commerceMarketing agencies, finance, retail, consulting

Remote Data Engineering focuses on designing and maintaining data infrastructure, while Remote Data Analysts interpret data to provide insights. Both roles require strong analytical skills but differ in technical depth and responsibilities.

What are the most commonly searched types of Data Engineering jobs in Connecticut? The most popular types of Data Engineering jobs in Connecticut are:
What are popular job titles related to Remote Data Engineering jobs in Connecticut? For Remote Data Engineering jobs in Connecticut, the most frequently searched job titles are:
What job categories do people searching Remote Data Engineering jobs in Connecticut look for? The top searched job categories for Remote Data Engineering jobs in Connecticut are:
What cities in Connecticut are hiring for Remote Data Engineering jobs? Cities in Connecticut with the most Remote Data Engineering job openings:
Remote Data Engineering jobs near you
Infographic showing various Remote Data Engineering job openings in Connecticut as of July 2026, with employment types broken down into 72% Full Time, 16% Part Time, and 12% Contract. Highlights an 100% Remote job distribution, with an average salary of $123,397 per year, or $59.3 per hour.
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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Benefits

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Get the full story on Breakroom

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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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