Job description

#### About the Role 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. Engineering data from Cameo, Teamcenter, and Siemens Capital arrives in Silver as the ingestion team lands it. Someone has to turn it into data the business can use: conformed, correctly keyed, documented, and with the business meaning defined once so every consumer, from a SQL analyst to a Genie space, gets the same answer. You will own that layer. You will build the curated Gold tables, define the semantic layer on top of them in Unity Catalog, and work with the engineering teams to settle what the data means. Next year, this layer becomes the foundation of the digital thread schema that connects requirements, models, parts, and designs across systems. #### What You'll Do * Implement part and revision keying across Cameo, Teamcenter, and Capital so the same part is one part everywhere. * Build Silver-to-Gold transformations in Databricks declarative pipelines, with data quality expectations on keys, relationships, and completeness, applied with MERGE so reruns are idempotent. * Define the semantic layer in Unity Catalog: metric views with measures and dimensions defined once, registered table relationships, business descriptions, and synonyms. * Publish Gold tables as governed data products in Unity Catalog, with documentation, tags, and ownership. * Work with source system SMEs to resolve how Cameo, Teamcenter, and Capital represent the same engineering concepts differently. * Lay the groundwork for the 2027 digital thread schema: keep keys, relationships, and definitions program-agnostic so they extend to traceability across systems. * Document data models, business rules, and lineage for handoff. #### What We're Looking For * 6+ years in data engineering, including production data modeling on Databricks or a comparable lakehouse. * Experience building a semantic layer: Unity Catalog Metric Views, or an equivalent such as dbt Semantic Layer, LookML, or Power BI semantic models. * Experience keying the same entity across multiple source systems, such as part number plus revision. * Working fluency in PLM and MBSE concepts and vocabulary, such as parts, BOMs, revisions, change objects, SysML model elements, and requirements traceability. * Experience with Databricks declarative pipelines or Delta Live Tables, including data quality expectations and MERGE into Gold. * Experience publishing governed tables in Unity Catalog with documentation, tags, and ownership. * Strong PySpark and SQL, and experience with Delta Lake and medallion architecture. * Experience working directly with engineering stakeholders to define data definitions and business rules. * Ability to complete the client's background check and onboarding and to work on client-furnished equipment. #### Nice to Have - Relationship-heavy or graph-like data modeling and entity resolution across systems. - Hands-on experience with data from Cameo / Teamwork Cloud, Teamcenter, or Siemens Capital. - Familiarity with digital thread or digital twin reference architectures. - Experience with SLICwave or logistics support analysis data. - Prior work in a defense, aerospace, or FedRAMP environment, or with CUI or ITAR-controlled data. - Databricks Data Engineer Professional certification. #### Why Lumenalta is an amazing place to work at At Lumenalta, you can expect that you will: - Be 100% dedicated to one project at a time so that you can innovate and grow. - Be a part of a team of talented and friendly senior-level developers. - Work on projects that allow you to use leading tech. For Databricks practitioners specifically, we are a Databricks partner with Champions and MVPs on our team, and that carries three concrete things: - **Delivery Partner Program qualification**. Databricks co-delivers its Professional Services engagements through a short list of approved partner organizations, and practitioners have to be individually qualified before they can work on them. The qualification is granted once at the individual level and does not expire, so it stays with you. Partner organizations are the route in, so the qualified pool stays small. That scarcity is most of the value, and we sponsor it. - **Champion path**. Databricks Champion is an individual recognition with its own badge, a dedicated Databricks solutions architect as your mentor, access to product teams, confidential roadmap briefings, and an invitation to the internal Tech Summit. Nomination has to come from a partner organization, which puts it out of reach for independents and most contractors. We nominate actively and regularly, and the Champions already on our team mentor candidates through it. - **Certifications covered.** We pay the exam fee for any Databricks certification you want to take. #### Our Process A screening call, a technical interview, and a HackerRank assessment. The technical interview is a conversation rather than a whiteboard exam. We will ask you to walk through a data model you designed across multiple engineering systems, how you keyed parts and revisions, how you validated traceability, and what you would change. Relevant project experience matters more to us than certifications. #### Location This is a fully remote position. This position supports a U.S. Government contract that requires all personnel to possess U.S. Citizenship. This role is remote within the United States only. Candidates must reside in the United States while assigned to our client and be physically located within the United States when performing project work or accessing project systems or data. Availability to work overlapping Pacific, Central, or Eastern U.S. time zones. #### Application Deadline Applications will be accepted until **October 25th, 2026**. Candidates can expect feedback by **November 2nd, 2026**.

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#### About the Role
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.
#### What You'll Do
Implement part and revision keying across Cameo, Teamcenter, and Capital so the same part is one part everywhere.
Build Silver-to-Gold transformations in Databricks declarative pipelines, with data quality expectations on keys, relationships, and completeness, applied with MERGE so reruns are idempotent.
Define the semantic layer in Unity Catalog: metric views with measures and dimensions defined once, registered table relationships, business descriptions, and synonyms.
Publish Gold tables as governed data products in Unity Catalog, with documentation, tags, and ownership.
Work with source system SMEs to resolve how Cameo, Teamcenter, and Capital represent the same engineering concepts differently.
Lay the groundwork for the 2027 digital thread schema: keep keys, relationships, and definitions program-agnostic so they extend to traceability across systems.
Document data models, business rules, and lineage for handoff.
#### What We're Looking For
6+ years in data engineering, including production data modeling on Databricks or a comparable lakehouse.
Experience building a semantic layer: Unity Catalog Metric Views, or an equivalent such as dbt Semantic Layer, LookML, or Power BI semantic models.
Experience keying the same entity across multiple source systems, such as part number plus revision.
Working fluency in PLM and MBSE concepts and vocabulary, such as parts, BOMs, revisions, change objects, SysML model elements, and requirements traceability.
Experience with Databricks declarative pipelines or Delta Live Tables, including data quality expectations and MERGE into Gold.
Experience publishing governed tables in Unity Catalog with documentation, tags, and ownership.
Strong PySpark and SQL, and experience with Delta Lake and medallion architecture.
Experience working directly with engineering stakeholders to define data definitions and business rules.
Ability to complete the client's background check and onboarding and to work on client-furnished equipment.
Relationship-heavy or graph-like data modeling and entity resolution across systems.
Hands-on experience with data from Cameo / Teamwork Cloud, Teamcenter, or Siemens Capital.
Familiarity with digital thread or digital twin reference architectures.

Nice-to-haves

#### Nice to Have

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sql

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Effort: medium

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