Sr. Staff Data Engineer (Tech Lead) - Hybrid

Sr Staff Data Engineer - GE07DE

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.   

         

Data, AI and Analytics Office (DAO) - Customer Data Ecosystem Operations Business Intelligence  -  is looking for a seasoned Technical Lead (TL)  to join the team. As a Tech Lead, you will work on leading the team in the development of Data Domains, Data Products along with analyzing, and visualizing large sets of data to turn information into insights using multiple platforms. You will develop and implement Proof of Concepts (PoCs), generic frameworks, and real-time pipelines using SQL, Amazon Web Services (AWS), ETL tools, Snowflake, and Python. You will use automation of software application development using Continuous Integration/Continuous Delivery (CI/CD) methodologies. Additionally, you will get opportunities to work on Machine learning & Artificial Intelligence initiatives.

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

Responsibilities:

• Modernize and implement the transformation roadmap for Enterprise Business Operations Billing Team using a new data reference architecture leveraging AWS cloud, Python and Snowflake. Transformation will focus on addressing challenges across legacy tech stack, data freshness issues, speed of deliver and quality.
• Integrate and aggregate complex data from multiple data sources and platforms to enhance customer access to information and promote a data-driven culture.
• Implement end-to-end generative AI pipelines, from data ingestion to pipeline deployment and monitoring.
• Develop and optimize RAG architectures and pipelines, Agentic Workflows and unstructured data processing
• Incorporate core data management competencies, including data governance, data security, and data quality.
• Develop and deploy analytical solutions leveraging machine learning technologies.
• Develop and support the migration activities of reporting assets from legacy sources to Snowflake.
• Collaborate with the Enterprise Data teams to provide user acceptance testing, maintain data quality, and advance the technical toolset.
• Develop and maintain data analytics tools and frameworks to support Artificial Intelligence & Machine Learning use cases.
• Stay up to date with emerging data technologies and industry best practices.
  Demonstrate a strong willingness to explore and leverage new tools and technologies as per project needs.
• Confident, self-starter capable of independently driving multiple concurrent projects to completion.

Qualifications:

• Candidates 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.

• Bachelor’s degree in computer science, Data Engineering, or a related field
• Minimum 2 years of experience as a Data Engineer, with a strong track record in quantitative, analytical and data manipulation skills
• Experience with ETL tools (Informatica, IDMC etc.)
• Unit, interface and end user testing concepts and tooling (functional & non-functional) 
• Knowledge on any Cloud tech stack AWS, Azure etc.
• Advanced knowledge of SQL as it pertains to data, analytics, and reporting on any relational database Oracle, SQL Server, Snowflake etc.
• Experience with any scripting or programing language – Python, JavaScript etc.
• Experience with Test automation & DevOps tools
• Knowledge of Agile Scrum/SAFE methodology
• Effectively use collaboration tools like Rally, Jira etc.
• Implement end-to-end generative AI pipelines, from data ingestion to pipeline deployment and monitoring.
• Develop complex AI systems, adhering to best practices in software engineering and AI development.
• Work with cross-functional teams to integrate GenAI solutions into existing products and services.
• Keep up-to-date with GenAI advancements and apply new technologies and methodologies to our systems.
• Assist in mentoring junior AI/data engineers in GenAI development best practices.
• Implement and optimize RAG architectures and pipelines.
• Develop solutions for handling unstructured data in AI pipelines.
• Implement agentic workflows for autonomous AI systems.
• Develop graph database solutions for complex data relationships in AI systems.
• Integrate AI pipelines with Snowflake data warehouse