Sr. Staff Data Engineer (Tech Lead) - Hybrid

The Hartford•Columbus, OH

2d•Hybrid

About The Position

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. Enterprise Data Services - Sales & Distribution IT is undergoing a transformation to the Cloud, and we are looking for an enthusiastic Sr. Staff Data Engineer to join our team. In this role, you will be leading the efforts to develop, enhance, and support new and existing ETL data pipelines, ingestions, and storage across various medium and large sized projects concurrently. This is an individual contributor position responsible for expanding and optimizing data pipeline and product architecture, as well as optimizing data flow and collection for cross functional teams. The ideal candidate is an experienced data pipeline builder and data wrangler who enjoys optimizing data systems and building them from the ground up. You will work on implementing complex data projects, focusing on collecting, parsing, managing, analyzing, and visualizing large sets of data to turn information into insights using multiple platforms. You will support our software developers, data architects, analysts and data scientists on vital initiatives and ensure optimal data delivery architecture. You will lead the design of vital components and suggest/implement new frameworks and tools based on industry and technological trends and advances. You will also consult with process owners in reviewing, interpreting & developing systems in accordance with user requirements and ensuring code quality through proper documentation, best practices, and code reviews. Working in our collaborative environment, you will further develop your skills while also acting as a mentor and technical guide to other data engineers. This role can have a Hybrid work schedule. Candidates who live near one of our office locations (Charlotte NC, Hartford CT, Chicago, IL, Columbus, OH) will have the expectation of working in an office 3 days a week (Tuesday through Thursday)

Requirements

Bachelor’s or master’s degree in computer science or a related discipline.
7+ years of experience in data analysis, transformation, and development, with ideally 2+ years in the insurance or a related industry.
5+ years of strong proficiency in SQL, Python, and ETL tools such as Informatica IDMC for data integration and transformation.
3+ years of experience developing and deploying large-scale data and analytics applications on cloud platforms such as AWS, GCP and Snowflake.
Experience in small or medium-scale Generative AI (GenAI) integration within data workflows or enterprise solutions.
3+ years of expertise in designing and optimizing data models for Data Warehouses, Data Marts, and Data Fabric, including dimensional modeling, semantic layers, metadata management, and integration for scalable, governed, and high-performance analytics.
3+ years of experience processing large-scale structured and unstructured data in both batch and near-real-time environments, leveraging distributed computing frameworks and streaming technologies for high-performance data pipelines.
3+ years of experience in Agile methodologies, including Scrum and Kanban frameworks.
2+ years of experience in leveraging DevOps pipelines for automated testing and deployment, ensuring continuous integration and delivery of data solutions.
Experience accessing and retrieving data from disparate large data sources.
Proficient in data visualization tools such as Tableau and Power BI, with expertise in creating interactive dashboards, reports, and visual analytics to support data-driven decision-making.
Ability to analyze source systems, provide business solutions, and translate these solutions into actionable steps.
Candidate must be authorized to work in the US without company sponsorship.
The company will not support the STEM OPT I-983 Training Plan endorsement for this position.

Nice To Haves

Knowledge of data observability (metrics, tracing, logs) and monitoring frameworks.
Exposure to metadata management (catalogs, glossary) and data lineage tools.
Familiarity with containerization & orchestration (Docker, Kubernetes).

Responsibilities

Modernize legacy data assets by migrating and re-engineering them into modern cloud solutions (AWS, GCP, Snowflake) for scalability, security, and cost efficiency.
Design, develop, and optimize ETL/ELT pipelines for structured and unstructured data, ensuring resilience and high performance.
Build and manage data pipelines leveraging cloud services such as AWS Glue, EMR, Redshift; GCP BigQuery, Dataflow; and Snowflake.
Curate and publish Data Products to support analytics, visualization, machine learning, and Generative AI use cases.
Implement DataOps practices for automated deployments, CI/CD integration, and continuous delivery of data solutions.
Apply best practices for data modeling, governance, and security, ensuring compliance with enterprise standards and regulatory requirements.
Establish and enforce Data Governance frameworks, including: Data Quality Management Metadata Management Data Lineage Tracking
Integrate AI/ML models into data pipelines, enabling real-time scoring, feature engineering, and model retraining workflows.
Enable Generative AI capabilities by embedding LLMs into data workflows for intelligent automation and advanced insights.
Develop and deploy AI Agents for automated decision-making and conversational analytics.
Lead Proof of Concepts (POCs) and pilot initiatives for emerging technologies—beyond GenAI—such as real-time streaming, AI agents, and next-gen data platforms.
Develop and maintain BI dashboards and visualization solutions using tools like Power BI or Tableau to deliver actionable insights.
Monitor and fine-tune data pipelines for performance, scalability, and reliability using advanced observability tools.
Automate auditing, reconciliation, and data quality checks to maintain high data integrity.
Develop self-healing pipelines with robust re-startability mechanisms for resilience.
Schedule and orchestrate complex workflows using tools like MWAA, Autosys, or Control-M.
Champion continuous improvement and innovation by adopting emerging technologies in DataOps, AI/ML, and cloud engineering.