Top Senior Data Engineer Jobs in Hartford, CT

The Data Engineer III will lead the development and maintenance of data infrastructure and tools, ensuring data integrity while driving strategic data initiatives.

The role involves building scalable ELT/ETL pipelines on GCP, utilizing SQL and Python, and managing security and infrastructure automation while ensuring real-time processing and resource provisioning.

Design, build, and automate scalable batch and streaming data pipelines, implement data models and ETL, automate deployments, support ML/experimentation, maintain server architecture, and partner with stakeholders to deliver analytics and data-driven tools.

Lead the design of DataOps platforms, focusing on scalable data infrastructures and automation frameworks. Oversee robust data orchestration and establish quality frameworks.

The Data Engineer will design and maintain data pipelines, enhance the data warehouse, support machine learning applications, and collaborate with various teams to leverage data effectively.

As a Senior Forward Deployed Data Engineer, you will design and implement data pipelines for media agency clients, manage technical delivery, and ensure data security while mentoring junior engineers and communicating effectively with stakeholders.

As a Data Engineer, you will design and maintain data pipelines, support DoD systems, and ensure data quality and compliance.

The Data Engineer is responsible for designing data workflows and pipelines, implementing systems engineering principles, and deploying data streaming technologies while ensuring compliance with industry regulations and best practices.

Forward-deployed senior data engineer who deploys, hardens, integrates, and operates a data platform (lakehouse, streaming, governance) in DoD environments. Build pipelines across classification boundaries, produce accreditation evidence (STIG/cATO/FIPS), run day-2 operations, train mission teams, and mentor junior FDEs while feeding field feedback into product improvements.

Design, deploy, and maintain scalable data infrastructure and orchestration for analytics and ML; write optimized SQL over large warehouses; partner with engineering and product to structure source systems; maintain CDC pipelines; scale reporting and expose analytical datasets enterprise-wide while ensuring data management, security, and compliance.

Design and build large-scale data migration systems, map heterogeneous legacy sources into the platform, ensure end-to-end data correctness, create repeatable tooling, support analytics and BI, optimize performance and storage, debug production data issues, and collaborate with Product, Implementation, and Ops for agency onboarding.

Design, operate, and scale end-to-end data infrastructure and pipelines. Build reusable data models and a semantic layer, ensure data quality and privacy, implement observability and SLAs, support marketing and growth feeds, partner with cross-functional stakeholders, and enable ML/AI consumption and reverse ETL workflows.

Lead and architect large-scale data products and pipelines across teams. Design and deliver ELT/ETL solutions, evaluate tools, ensure scalability and integrity, influence technology decisions, and research emerging big-data/cloud methodologies (AWS, Hadoop/EMR, Spark, Kafka, Snowflake). Partner with architects and stakeholders to drive implementation and operationalize production workloads.

Own and architect the semantic layer and metrics/metadata for the data platform (Databricks/GCP). Build AI-ready models, enforce modeling and documentation standards, implement observability/testing for pipelines, enable self-serve analytics, and collaborate across product, clinical, and business teams to translate questions into reliable data solutions.

As a Senior Data Engineer, you'll transform raw data into datasets, design production pipelines, integrate customer data, and collaborate with various teams to ensure data quality and reliability.

Lead design, development, and implementation of AWS- and Snowflake-based data solutions. Build and optimize data pipelines, ensure data quality and security, mentor junior engineers, and translate business requirements into scalable data architectures, with insurance domain expertise preferred.

Design, build, and maintain scalable ETL/ELT pipelines and product-level data architectures. Improve pipeline performance and data freshness, lead code/design reviews, mentor engineers, and collaborate with Data Science, Product, and Engineering to make data reliable and accessible.

Design, build, and maintain scalable, secure data architectures, models, and ETL pipelines. Integrate diverse data sources, optimize performance and data quality, implement governance, monitor production SLAs, and collaborate with data scientists, analysts, and product teams. Provide technical leadership, documentation, and training to improve engineering practices across the organization.

Build and enhance enterprise analytics and visualization solutions: develop interactive dashboards, data-driven apps, transform and prepare data with Python, implement frontend visualization components with TypeScript, integrate with Microsoft and Google enterprise ecosystems, and use AI tools to accelerate development while collaborating with stakeholders and owning deliverables end-to-end.

Own and operate production data pipelines and scheduled workflows supporting billing, analytics, and reporting. Monitor, troubleshoot, and respond on-call to incidents; implement data quality checks, SOX controls, and pragmatic modernization. Improve orchestration, automate manual steps, optimize data models and infrastructure, and collaborate with analysts, data scientists, and stakeholders while mentoring junior engineers.

Build and maintain GCP-based data pipelines and foundational tables (BigQuery, Dataform, Dataplex). Merge financial and ERP datasets, define metrics, deliver reports and analyses, and collaborate cross-functionally to support decision-making and business measurement.

As a Senior Data Engineer, you will build complex data solutions and pipelines, support analytics growth, and educate users. Collaboration and advanced technical skills are essential.

Design, build, and operationalize scalable data pipelines and transformations on cloud platforms. Ensure data governance, quality, and security; collaborate with data science to support ML model operationalization; mentor engineers and drive architectural decisions for enterprise analytics.

Design, build, and operate production data pipelines across AWS, Databricks, and Snowflake. Modernize and migrate legacy AbInitio workloads, embed data quality/observability/lineage, optimize cost/performance, drive CI/CD and DataOps practices, integrate AI coding tools, mentor engineers, and standardize reusable pipeline frameworks.

Lead a team of 5-6 data engineers to design and implement scalable data pipelines (Airflow), manage Snowflake data architecture, build real-time streaming solutions with Kafka/Spark Streaming, develop and integrate APIs, enforce code quality, and drive technical decisions and best practices.