Work Based Learning Program Aws Jobs in Hartford, CT

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The annual base salary range provided for this position is a nationwide market range and represents a broad range of salaries for this role across the country. The actual salary for this position will be determined by a number of factors, including the scope, complexity and location of the role; the skills, education, training, credentials and experience of the candidate; and other conditions of employment. As part of our comprehensive compensation and benefits program, employees are also eligible for performance-based cash incentive awards.

• Design and build production data pipelinesacross AWS, Snowflake, Databricks supporting both batch and near real-time analytics workloads.
• Establish reusable engineering patterns and frameworks - Parameterized, modular, idempotent pipeline templates that other engineers adopt, reducing duplicated effort and inconsistent implementations.
• Drive down lead timefrom commit to production by removing manual steps, leveraging AI, building self-service tooling, and standardizing the path to deployment; treat cycle time as a metric you actively own and improve.
• Champion SDLC disciplinecoveringversion control, peer code review, automated testing, environment promotion, change management, and documentation.
• IntegrateAI codingtools into daily workflowto accelerate scaffolding, refactoring, test generation, code optimization, and documentation, with measurable and demonstrable impact on throughput and quality.
• Measure and demonstrate impact, tying AI-tool adoption to concrete outcomes such as reduced lead time, faster test coverage, and improved consistency, and sharing those results to drive broader adoption.
• Evaluate emerging toolingand make pragmatic recommendations on what engineersshould adopt, standardize on, or avoid.
• DataOps- Blur the lines between data and software engineering practices. Employ CI/CD, automated testing, and apply trunk-based or short-lived branch development to data the same way it is to software.
• Modernize legacy workloads- Help manage and optimize Ab>Initio pipelines and when applicable, help migrate or re-platform Ab>Initio pipelines toward cloud-native, declarative, ELT-based patterns on Snowflake and Databricks where it delivers value.
• Embed data quality, observability, andlineageinto pipelines as a default, not an afterthought, automated data tests, freshness/quality SLAs, and traceable lineage.
• Optimize for cost and performanceacross Snowflake compute, Databricks clusters, and storage, applying FinOps-aware engineering practices.
• Mentor and upskill engineersthrough code review, pairing, design guidance, and documented standards, acting as a technical multiplier for the team.

• Bachelor's Degree in STEM related field or equivalent
• Ten years of related experience building,designing and operating production data pipelines at scale.
• Demonstrable experience architecting, designing and building scalable, secure data solutions using AWS, Databricks, Snowflake and Ab>Initio or similar platforms.
• A track record of leveraging AI assistants, create skills/tools to augment data engineering practices throughout the development lifecycle.
• The ability tolead technical direction for data engineering initiatives across cloud and on-premises infrastructure.
• Willingness to run mentoring sessions and offer technical guidance to the 20-person admin team.
• Ability to manage infrastructure deployment and optimize cloud resources.
• Drive to learn, identify and set technical standards and influence engineering practicesand data governance policies.
• Abilityto lead and take action even when there is no clear owner, inspire and motivate others, and be effective at influencing team members.
• Technical Skills:
  • Cloud:Proficiency with commonly used AWS services and architectures for data and analytics solutions
  • Databricks:Workspace management, cluster configuration, open table formats (Iceberg, Delta Lake), Unity Catalog, building and tuning Spark/pySpark/SQL workloads.
  • Data Warehousing:Strong understanding of data modeling, dimensional modeling (star/snowflake schemas) and medallion (bronze/silver/gold) architecture.
  • AI Coding Assistants:Familiarity using Claude Code, Codex, Copilot, Cursor, etc. for day-to-day engineering tasks.
  • AbInitio:Proficiency with GDE, Co>Operating System, and EME, including maintaining and optimizing existing graphs.
  • CI/CD for data: Git-based workflows, branching strategy, and pipeline automation (e.g., GitHub Actions, GitLab CI, or Jenkins).
  • Infrastructure as Code (e.g., Terraform): Familiarity witha configuration-driven, repeatable approach to environments

• Bachelor's degree in computer science, related STEM field, or its equivalent in education and/or work experience.
• 6 additional years of data engineering experience.