Top Data Engineer Jobs in Hartford, CT

Design and build data infrastructure, pipelines, and integration solutions using cloud and big-data tools. Develop data lakes/warehouses, ensure data quality and security, apply data modeling and DAGs, use Databricks, Airflow, and Hadoop, and collaborate with clients to deliver actionable insights.

Lead design and implementation of data infrastructure, pipelines, and integrations using cloud platforms. Manage teams and client accounts, ensure data quality, security, and compliance, deploy scalable solutions (Databricks, Snowflake), mentor junior staff, and identify data-driven business opportunities.

The Data Engineer will build and maintain data pipelines, optimize workflows, manage infrastructure, and enhance pipeline observability while collaborating with teams for effective data delivery.

Build and maintain reliable data pipelines, Airflow DAGs, and Snowflake-based Data Vault/warehouse models. Implement CI/CD, automated testing, observability, and production support while partnering with stakeholders and developing insurance domain expertise.

As a Data Engineer II, you will build, scale, and optimize data platforms, focusing on ETL/ELT pipelines, and collaborate with data scientists and AI engineers to support data-driven applications and insights.

As a Senior Associate in Data Engineering at PwC, you will design and implement data architecture strategies, analyze complex issues, mentor junior members, and build client relationships while ensuring adherence to project standards.

As a Senior Manager in Data Engineering, you will lead teams to design data architectures, develop data solutions, manage client engagements, and ensure compliance with data governance policies.

Lead data engineering projects, develop and implement data pipelines and architecture strategies, manage data quality, and mentor team members at PwC.

As a Data Engineer at Edmunds, you will architect and maintain data platforms for analytics and AI/ML, ensuring scalable, reliable data processing and supporting cross-functional collaboration.

The Data Engineer will help build modern data infrastructure, designing ETL pipelines, modeling datasets, and collaborating with teams for improved data quality and accessibility.

Design, build, and maintain scalable data assets and pipelines for analytics and ML; validate and ingest diverse data sources; perform exploratory data analysis and data quality checks; collaborate with cross-functional teams and produce reproducible code and documentation.

The BI ETL Data Developer II/III develops and maintains scalable data pipelines in a cloud-based environment, enhancing data integrity and quality for informed decision-making across the University.

Build scalable, secure data pipelines and ETL/ELT processes in cloud environments to standardize and integrate data, create data models/marts, support data science, automate workflows, and improve data accessibility across the enterprise.

As a Data Engineer II, you will build and operationalize complex data solutions, ensuring data quality and governance while supporting AI and business intelligence applications.

Lead design and implementation of shared, reusable analytics data models and pipelines. Drive standardization, governance, observability, and CI/CD for analytics; partner with Data Science, Infrastructure, and Product to certify metrics, modernize orchestration, and integrate AI-native tooling.

As the founding engineer of the Data Intelligence team, you will design semantic layers, develop AI tooling, and automate data processes to drive business insights.

As a Data Engineer I, you will design, build, and operationalize data solutions, manage data governance, and collaborate with teams to optimize analytics processes. Responsibilities include developing pipelines, troubleshooting issues, and mentoring team members.

Design, build, and maintain ETL pipelines and data infrastructure to support analytics and reporting. Write and schedule workflows (Airflow DAGs), manage third-party data ingestion, implement data validation, optimize performance, and collaborate on data models, governance, and ML model deployments.

As the first in-house Data Engineer, you will own the data infrastructure, build and maintain data pipelines, and collaborate with teams to ensure quality data access and insights.

Lead architecture and implementation of scalable data pipelines, integrations, and cloud data infrastructure. Own IaC (Terraform), workflow orchestration (near-real-time and batch), third-party API and AI/LLM integrations, data export pipelines, and internal tooling. Mentor engineers, drive technical strategy and cross-team priorities, and communicate tradeoffs to technical and non-technical stakeholders. Must work EST/CST hours and be authorized to work in the US.

As a Senior Data Engineer at Jellyfish, you'll build and maintain data pipelines, optimize orchestration, automate CI/CD processes, and enhance data integration while ensuring high performance and reliability.

Design, build, tune, and maintain Oracle-based data warehouse solutions and ETL processes. Develop PL/SQL, SQL, SSIS packages, and Unix scripts; manage jobs, scheduling, and monitoring; perform performance tuning, debugging, and production support; collaborate with DBAs, analysts, and teams to implement and document robust data solutions while meeting SLAs.

Design, build, and maintain scalable Spark-based ETL pipelines and computed tables in a central data lake. Integrate structured and unstructured IoT, sensor, and external data for analytics, model training, and dashboards. Collaborate with Data Science, Analytics, and ML teams to ensure reliable, high-quality customer-facing datasets.

Build and maintain ETL/ELT pipelines, ingest and transform structured and unstructured data, integrate APIs and external sources, improve data quality and monitoring, support dashboards and ML pipelines, optimize warehouse and cloud performance, and contribute to data governance and documentation.

Design, build, and maintain production-grade ETL/ELT pipelines and data models using dbt, Spark, and cloud platforms. Manage ingestion, data quality, observability, semantic layers, and BI outputs. Collaborate with analysts and data scientists to deliver governed, high-quality data products and implement DataOps and CI/CD practices.