Data Engineer

Responsibilities

• Design, build, and maintain scalable ETL/ELT data pipelines and integrations.
• Develop and support data ingestion, transformation, and delivery solutions using cloud-native technologies.
• Implement data quality controls, monitoring, and observability capabilities to ensure reliable data products.
• Support machine learning and AI solutions through data engineering, feature engineering, and operationalization activities.
• Build reusable frameworks, components, and automation capabilities to increase delivery efficiency.
• Collaborate with Data Science, Enterprise Data, Cloud Enablement, Architecture, and Business teams to deliver data solutions.
• Develop and maintain CI/CD pipelines and Infrastructure as Code (IaC) assets to support cloud-based deployments.
• Assist with the deployment, monitoring, and support of production data and AI services in AWS and GCP environments.
• Troubleshoot and resolve data pipeline, integration, and platform performance issues.
• Participate in Agile ceremonies, code reviews, technical documentation, and continuous improvement activities.
• Follow and promote software engineering, DataOps, and MLOps best practices.

Requirements

• Must be authorized to work in the U.S. now and in the future.
• Bachelor's degree in Computer Science, Data Engineering, Information Systems, or related field, or equivalent work experience.
• Experience building and supporting data pipelines in cloud-based environments.
• Experience with SQL development and relational database concepts.
• Experience with Python or similar programming languages.
• Familiarity with AWS and/or GCP cloud services.
• Experience with source control systems such as GitHub.
• Experience with CI/CD tools such as GitHub Actions, Jenkins, or similar platforms.
• Experience with Infrastructure as Code (Terraform, CloudFormation, or similar technologies).
• Familiarity with workflow orchestration tools such as Apache Airflow, Cloud Composer, or similar platforms.
• Experience working with data warehouse technologies such as Snowflake, Redshift, BigQuery, or similar platforms.
• Understanding of data quality, data governance, and data lifecycle management principles.
• Familiarity with API integration and cloud-native application development concepts.
• Basic understanding of machine learning workflows and model deployment concepts.

Qualifications

• 2+ years of experience in data engineering, software engineering, analytics engineering, or related technical roles.
• 2+ years of Python development experience.
• 2+ years of SQL development experience.
• Experience developing, maintaining, or supporting ETL/ELT data pipelines.
• Experience working with cloud technologies such as AWS, GCP, or Azure.
• Experience using CI/CD pipelines and Infrastructure as Code practices.
• Experience working with modern data platforms such as Snowflake, BigQuery, or Redshift.
• Exposure to data quality, monitoring, and operational support processes.
• Familiarity with emerging data-centric technologies including Generative AI, Agentic workflows, and embedding LLMs into automated processes.
• Experience working in Agile development environments.
• Experience in highly regulated industries such as insurance or financial services.

Pay

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: $100,960 - $151,440.

Schedule

This role will have a Hybrid work schedule, with the expectation of working in an office (Columbus, OH, Chicago, IL, Hartford, CT or Charlotte, NC) 3 days a week (Tuesday through Thursday).