Senior Databricks Data Engineer, Spreadsheet File Sources

We partner with global enterprises to design and build large-scale data platforms that power the products and operations at the center of their business.

Not all of that data lives in enterprise systems. Manufacturing and supply chain teams maintain parts, supplier, and program data in Excel workbooks and flat-file repositories. These are authoritative sources, but they have no schema, no change log, and no owner-enforced structure. Each workbook is a small, undocumented system of its own.

You will bring those sources onto the platform. You will inventory them, build ingestion that turns inconsistent workbooks and files into governed Delta tables, and make the result join cleanly to parts and revisions from the engineering systems, so the digital thread includes the data that today lives only in spreadsheets.

• Inventory the Excel workbooks and file exports that business and engineering teams rely on, and document their structure, owners, and downstream use.
• Build Auto Loader pipelines to ingest workbooks and flat files landed in S3, with explicit schemas and typed Bronze and Silver tables.
• Parse multi-sheet, inconsistently structured workbooks at scale: shifting header rows, merged cells, embedded totals, mixed types in a column, and layouts that differ between versions of the same file.
• Configure schema evolution in Databricks declarative pipelines, including new column handling, rescued data columns, and controlled failure on renamed columns.
• Implement data quality expectations that catch the failure modes these sources produce, such as duplicate rows, blank keys, and silently changed formats.
• Conform ingested data to the platform's part number plus revision keying so it joins to BOM and engineering data from other sources.
• Ensure lineage is captured for every pipeline, orchestrate with Databricks Workflows, and document each source for handoff.
• Work with the business owners of each workbook to confirm meaning, business rules, and what "correct" looks like.
• Translate spreadsheet logic, such as formulas, lookups, and pivot tables, into maintainable SQL and documented business rules.
• Validate that platform outputs match the numbers users trust today, and explain differences when they do not.
• Document datasets, metrics, and definitions so reporting stays consistent after handoff.

• Production experience building ingestion pipelines on Databricks.
• Hands‑on experience ingesting Excel and flat-file sources into Spark or Databricks at scale and the judgment to know when each is appropriate.
• A track record of making human‑maintained data reliable: shifting headers, merged cells, mixed types, and layout drift between versions.
• Experience with Auto Loader for incremental file ingestion from S3.
• Experience with Databricks declarative pipelines or Delta Live Tables, including schema evolution and data quality expectations.
• Experience conforming ingested data to shared keys so it joins with data from other systems.
• Strong PySpark and SQL, and working knowledge of Delta Lake and medallion architecture.
• Experience working directly with non-technical data owners to pin down definitions and business rules.
• Ability to complete the client's background check and onboarding and to work on client-furnished equipment.
• US citizenship.
• Fluent English, both written and spoken.

• Experience extracting from Microsoft Access databases (ODBC, JDBC, or file‑level tooling).
• Prior work with manufacturing or supply chain data, especially BOMs, parts, revisions, and supplier records.
• Familiarity with PLM or ERP data, such as Teamcenter or Oracle E‑Business Suite.
• Prior work in a defense, aerospace, or FedRAMP environment, or with CUI or ITAR‑controlled data.
• Databricks Data Engineer…