Top Senior 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.

Design, build, and optimize BigQuery-based data solutions and data models. Manage data storage, partitioning, clustering, ETL, and data quality. Collaborate with architects, data scientists, and engineers to deliver scalable, secure cloud data infrastructure and documentation; senior hires provide technical leadership, code review, and process improvement.

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.

Design, build, test, and operationalize data pipelines and solutions on cloud and Databricks. Ensure data quality, governance, security, and support incident resolution. Collaborate cross-functionally to deliver analytics, ML/AI enablement, and educate users on data products.

Design, build, test, and operationalize complex data pipelines and ETL solutions using Databricks and AWS. Analyze source data, enforce data governance, security and quality, resolve defects, collaborate with stakeholders, and support AI/ML and BI use cases.

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

Build user-facing data products by transforming internal data into actionable metrics. Collaborate with platform teams and data scientists to prototype, validate, and prioritize data sources. Write requirements and proposals, engage in user research, and deliver performant Python/SQL-based data features using Databricks and Postgres.

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.

Lead design and operation of large-scale data pipelines and data APIs. Build Spark/PySpark workflows on Databricks, optimize job performance, manage data quality and observability, and develop MCP servers and AI-agent integrations. Mentor engineers, define standards, support production incidents and on-call rotations, and collaborate with stakeholders to deliver scalable data platform products.

Owner of data platform modernization: design and migrate legacy systems to Kimball-style dimensional models and star schemas; build reliable production data pipelines; enforce data hygiene and validation; champion dbt best practices; mentor junior engineers; collaborate with analysts and stakeholders to ensure scalable, well-documented data models and platform observability.

Design, build, and optimize scalable batch and real-time data pipelines; develop ETL/ELT workflows; manage and tune cloud data warehouses; automate infrastructure with Airflow and IaC tools; deploy with containerization and orchestration; collaborate with data scientists and analysts; monitor, troubleshoot, and enforce data governance and security.

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.

Design, build, and operate large-scale data platforms and Spark/PySpark pipelines. Enable data integration, modeling, quality, and observability. Build MCP servers and AI-augmented tooling, mentor engineers, and lead cross-functional projects to deliver reliable data products.

Own and architect large-scale data pipelines, orchestration, and integrations. Drive IaC strategy (Terraform), third-party API and AI/LLM tool integrations, build export pipelines and internal tooling, mentor engineers, and align cross-team technical strategy for reliable, scalable data infrastructure.

Design, build, test, and operationalize complex data pipelines and transformations using ETL tools and cloud platforms. Ensure data quality, governance, and security while collaborating across teams, troubleshooting incidents, and mentoring engineers to deliver scalable data solutions for analytics and ML.

The Senior Bioinformatics Data Engineer will build data pipelines, implement clinical data ingestion, and optimize platform infrastructure while ensuring data quality and compliance with legal standards.

Design, build, test, and operationalize data pipelines and solutions. Analyze data sources, apply transformations and quality controls, resolve incidents, and collaborate with teams to deliver data products supporting analytics, BI, and AI/ML use cases.

Build, test, and operationalize complex data pipelines and solutions on cloud platforms. Analyze and transform data, ensure data governance, security, and quality, troubleshoot defects, and collaborate across teams to support analytics, ML/AI, and business intelligence needs.

Design, build, and maintain ingestion and transformation pipelines for a knowledge-graph analytics platform. Profile and prepare structured and semi-structured source data, ensure data lineage and traceability, implement transformation logic for graph loading, collaborate with data scientists on graph modeling and entity resolution, improve pipeline reliability, and document ingestion patterns using version control and issue tracking.

Design, build, and maintain Keboola-based data pipelines integrating multiple business systems to deliver accurate, scalable data for supply chain, logistics, and transportation analytics; collaborate with analysts and engineers; support documentation, governance, and pipeline automation.

Lead enterprise data platform strategy and architecture, define data standards and governance, drive Snowflake maturity, oversee legacy data migrations, design ingestion and DataOps patterns, and enable analytics, reporting, AI, and reusable data products across the organization.

Own and optimize data infrastructure and ETL/ELT pipelines using SSIS and Microsoft Fabric. Build and maintain SQL Server data warehouse staging, transformations, and lakehouse ingestion. Implement CI/CD, version control with GitHub, monitor pipeline health, troubleshoot data issues, document data flows, and translate stakeholder requirements into data engineering solutions that support BI reporting.