Data Engineer; Data Designing, ETL, ELT, Microsoft SQL Server, Oracle, Snowflake, AWS, Azure, d

Data Engineer – Data Designing, ETL, ELT, Microsoft SQL Server, Oracle, Snowflake, AWS, Azure, Dell Boomi (Middletown, RI)

Full‑time onsite position (no remote).

Salary:
Excellent compensation with benefits + 401K.

The Data Engineer will collaborate with business owners, users, consultants and technicians to assist in the implementation of a comprehensive corporate data strategy. The primary responsibility is to execute tasks along the roadmap and to ensure proposed solutions align to the corporate roadmap, principles, and policies. The Data Engineer will identify potential new areas for focus and create additional definition and clarity around existing areas that are not fully defined or understood.

• 5+ to 7 years of experience.
• Computer fundamentals – Knowledge or exposure to data modeling/design, ETL/ELT, Microsoft SQL Server, Boomi, Oracle, cloud databases (Snowflake, Redshift, Big Query) and cloud service providers (AWS, Azure).
• Data management – Demonstrated success in enterprise data management, integration, and data solutions.
• Collaboration – Ability to operate effectively in a team with limited direct guidance to execute the data strategy.

• Judgment & Decision Making – Utilizes technology and resources to deliver intelligent solutions.
• Oral & written communication – Provides clear verbal and written communication to business partners and other stakeholders.
• Internal customer satisfaction – Builds strong intercompany relationships to deliver quality solutions.

• Design, build, and improve data movement processes between on premise and off premise data stores. Primary toolsets: SQL Server, SSIS, Snowflake, Dell Boomi.
• Execute on corporate vision for data driven future.
• Foster positive business outcomes by executing the data strategy and roadmap, ensuring alignment across the organization.

• Complete tasks to ensure proposed solutions align with corporate roadmap, principles, and policies.
• Navigate short‑term business needs that evolve into long‑term solutions, aligning them with the overall data strategy and roadmap.
• Analyze cost/benefit of short‑term versus long‑term transition.

• Perform daily checks of success.
• Report on process performance, including development of metrics to act as early indicators of issues.
• Improve number of rollbacks, errors, average time to commit.