Senior Data Engineer

Offering competitive wages and benefits, that support your life both in and out of work

Providing a flexible hybrid work schedule, meaning we expect the office to be your primary place of work, balanced with choice and control.

Creating continuous learning opportunities to help you grow and upskill.

Fostering a culture of inclusion where employees feel seen, heard and valued — and living it out every day.

Empowering you to make a meaningful impact on people and the planet through your work and Steelcase’s ongoing commitment.

Collaborate with product owners, managers and engineers, help with scoping and defining Minimum Viable Products (MVPs).

Collaborate with multi-functional teams to define, design, and build big data solutions using tools and programming languages like Databricks, Azure Data Factory, Apache Spark, Python, SQL, etc.

Architect and develop scalable and robust data pipelines using data from diverse sources, databases, APIs, applications and files (e.g. Snowflake, Azure Synapse, AWS Redshift, GCP Big Query).

Apply data architecture patterns (e.g., event-driven, medallion, data lakehouse) to ensure scalability, performance, and maintainability of data solutions.

Perform data mapping, establish data lineage, define data contracts, and document information flows to ensure observability and traceability (e.g. Azure Purview, Lakehouse Monitoring).

Collaborate with data consumers (e.g. data analytics stakeholders and data scientists) to streamline the data acquisition and curation process.

Monitor, optimize and troubleshoot data pipeline performance issues and coordinate the issue resolution process with the respective individuals/partner teams.

Research and promote new tools and techniques to shape the future of the data platform, and build POCs to validate these new concepts including, but not limited to, data processing frameworks, distributed storage systems, data orchestration and workflow tools. (e.g. Databricks Lakebase, Azure Event Hubs, Azure Stream Analytics).

Implement an enterprise data governance model (e.g., Databricks Unity Catalog) and actively promote data protection, sharing, reuse, quality, and standards.

Architect, develop, and manage the data platform infrastructure, ensuring high availability, scalability, and security, utilizing multiple methods such as infrastructure-as-code (IaC) and CI/CD pipelines (e.g. Azure DevOps).

At least 3 years of full-time experience in US as Data Engineer, Data Scientist, AI Engineer, Software engineer or similar position.

Hands-on experience with Databricks, Apache Spark, Azure, and Python.

Strong understanding of data engineering concepts, including data pipelines and scalable data processing solutions.

Qualified applicants must be authorized to work in the United States on a full-time basis. Steelcase will not provide support for or sponsor work authorization and/or visas for this role.

Experience with Data Governance practices and frameworks.

Experience working with cloud platforms. While Azure experience is preferred, candidates with relevant experience in AWS or Google Cloud Platform (GCP) are also encouraged to apply.

Knowledge of Scala is preferred.