Job Description:
Summary:
The Data Engineer will design and deliver high-quality data solutions that advance the enterprise's data-driven decision-making capabilities. This will be accomplished through the development and optimization of scalable data pipelines, integration of diverse data sources, and the creation of curated data products that empower analytics and data science teams. The role requires strong technical expertise in data architecture, engineering, and analytics, as well as a collaborative mindset to work effectively within agile, cross-functional teams. This position will serve as a key contributor to the organization's analytics strategy by solving complex data challenges, fostering a culture of innovation and reusability, and supporting the implementation of modern data platforms and tools. The Data Engineer will also partner with business and technical stakeholders to ensure alignment with strategic initiatives and promote a robust, efficient, and forward-looking data ecosystem.
Responsibilities:
- Recognized internally as a key technical contributor in the data engineering domain, the Data Engineer plays a pivotal role in advancing the organization’s analytics capabilities. This position is accountable for the design, development, and optimization of data pipelines and architectures that support enterprise-wide analytic initiatives and strategic decision-making. The Data Engineer collaborates with cross-functional teams to translate complex business needs into scalable data solutions, applying critical thinking and technical expertise to solve data challenges with broad organizational impact. This role leads the implementation of modern data platforms and tools, influences data governance practices, and contributes to the evolution of enterprise data strategy. The Data Engineer anticipates emerging data trends and technologies, recommends enhancements to data infrastructure and processes, and communicates technical concepts clearly to both technical and non-technical stakeholders. Operating within a matrixed environment, the Data Engineer fosters a culture of innovation, reusability, and continuous improvement across the analytics ecosystem.
- Designs, develops, and maintains scalable data pipelines and architectures that support enterprise analytics and reporting needs. Applies deep technical expertise to ensure data solutions align with business goals and adhere to best practices in ETL, data modeling, and governance. Able to quickly learn and understand data from diverse sources such as third-party APIs, cloud or on-premise SQL databases, Excel files, etc.
- Collaborates with cross-functional teams—including business analysts, data scientists, and solution architects—to translate business requirements into robust data solutions that enable actionable insights and strategic decision-making.
- Collaborates with stakeholders and clients to translate opportunities to data solutions. Leverages effective communication strategies to accelerate strategic initiatives.
- Acts as the technical lead for projects involving integration and curation of data from diverse sources, ensuring data quality, consistency, and accessibility across platforms. Administers and optimizes data warehouse environments and cloud-based infrastructure to support high-performance analytics.
- Identifies and resolves complex data issues, proactively addressing data gaps, latency, and transformation challenges to ensure reliable and timely delivery of information.
- Champions a culture of reusability, scalability, and operational efficiency by developing standardized processes, reusable components, and documentation for data engineering practices.
- Evaluates and implements emerging tools and technologies in the data engineering space, contributing to the continuous evolution of the enterprise data ecosystem.
- Develops and delivers training and communication materials to promote understanding of data engineering capabilities, standards, and processes across the organization.
- Mentors junior team members and contributes to technical discussions such as regular peer code review, fostering a collaborative environment that supports innovation, learning, and continuous improvement.
- Maintains awareness of advancements in machine learning, artificial intelligence, and data science, and explores opportunities to integrate these capabilities into data engineering workflows.
How You’ll Be Successful:
Required Work Experience:
- At least 5 years in data engineering or data architecture role is required
- At least 5 years advanced to expert proficiency with SQL (e.g., sqlserver, Oracle, etc.) is required
- At least 3 years strong to advanced proficiency with Python - required
- At least 5 years developing and maintaining ETL/data pipelines (e.g., Microsoft SSIS, Azure Data Factory) is required
- At least 5 years developing and maintaining data warehouses in big data environments (e.g., Snowflake, SAP Hana, etc.) is required
Preferred Work Experience:
- At least 3 years proficiency with git for collaboration and version control is preferred
- At least 3 years working with cloud computing services and infrastructure in the data and analytics space is preferred
- At least 3 years using BI tools such as Tableau or Power BI is preferred
- At least 3 years working with data fabric technologies such as Denodo, Microsoft Fabric, etc. is preferred
- At least 1 year exposure to machine learning, data science, or artificial intelligence concepts is preferred
- At least 3 years' experience working in healthcare or other regulated industries is preferred
Education:
- Four-year bachelor's degree or equivalent experience In Computer Science, MIS, Engineering, or related field is required
Licenses And Certifications:
- Certified Data Management Professional (CDMP) or similar certification is preferred
- Microsoft Certified: Azure Data Engineer Associate is preferred
