Analytics Engineer (Remote)

Digital Strategy LLC is seeking a hands-on analytics engineer to design the analytical data models behind reporting and analytics for the U.S. Department of Energy’s Office of Critical Minerals and Energy Innovation (CMEI). Our data team is moving from an on-premises SQL Server and SSIS data warehouse to a DOE-wide enterprise platform built on AWS-hosted Databricks.

U.S. citizenship is required by the federal client. Lawful permanent residents (green card holders) and visa holders are not eligible for this position.

This role combines analytics engineering with the work of a dimensional data modeler, centered on the Gold (curated) and semantic layers. You will learn the business well enough to choose the right grain, define facts and dimensions, and build semantic models and governed metrics that BI developers can rely on. Partner teams handle source connectivity and enterprise ingestion, including the SQL Server migration feeds, so your work typically begins with data already on the platform. You will write the transformations from Bronze forward that feed your models, but success is measured by the quality and usefulness of the analytical model, not the number of source systems integrated.

Strong candidates often come from one of these backgrounds:

• Analytics engineering: You have owned dimensional models and semantic layers from business requirements through production.
• Data warehouse or BI engineering: You have built Kimball-style warehouses and data marts, SSAS Tabular or Power BI semantic models, or governed metrics layers that others reported from.
• Data engineering with Gold-layer depth: Your experience includes substantial dimensional design work, not only ingestion and integration.
• Analyst or SQL Server warehouse developer turned engineer: You bring strong business modeling instincts and the engineering skills to put models into production.

This role is built around analytical modeling. Experience with source-system integration or transactional (OLTP) database design is welcome, but this role requires that you have also designed dimensional models used for analytics.

Prior Databricks experience is valuable, not required. Hands-on Databricks work shortens your ramp-up so you can focus on learning CMEI’s business, but strong analytical modeling on Snowflake, Microsoft Fabric, Synapse, BigQuery, Redshift, SQL Server, dbt, or other enterprise platforms translates well.

This is an individual contributor role owning the models you build and working directly with federal client stakeholders, fellow engineers, data subject-matter experts, and BI developers., Model the business

• Start from the business question, not the source tables, working with stakeholders and subject-matter experts to understand the processes and decisions the data supports.
• Use fact-qualifier (measure-by-dimension) matrices in early model design so stakeholders agree on measures, qualifiers, hierarchy levels, grain, and conformed dimensions.
• Design fact tables at an explicit grain, using transaction, periodic snapshot, or accumulating snapshot patterns as each process requires.
• Design conformed dimensions, hierarchies, surrogate keys, and slowly changing dimensions, choosing star or snowflake structures deliberately.
• Build semantic models and governed metrics, including Databricks metric views, so a measure means the same thing across dashboards, Genie Agents (formerly Spaces), and applications.
• Document models and metrics in Unity Catalog, including table and column descriptions, so analysts and BI users can find, understand, and trust the data for self-service analysis.
• Absorb new datasets into increasingly cross-domain Gold and semantic models, extending conformed dimensions and shared metrics instead of building isolated marts.
• Redesign legacy SQL Server warehouse structures and report-layer logic into sound Gold and semantic models rather than carrying them over as-is.

Build, validate, and deliver the models

• Translate business rules into testable logic, and reconcile model outputs to source totals using automated tests, pipeline expectations, and AI-assisted checks.
• Develop SQL and PySpark transformations from Bronze forward, including occasional public datasets.
• Diagnose data and metric defects and fix them at the right layer, not in reports.
• Use source control, code review, CI/CD, and documentation, coordinating promotion across environments with the DOE platform team.
• Partner with BI developers so BI products rest on a model that answers business questions.
• Explain modeling trade-offs clearly to business users and technical peers.

First six months

• Learn CMEI’s mission domain and business rules well enough to model them, along with team standards and the Databricks environment.
• Deliver a production Gold or semantic model with documented grain, business rules, and quality tests.
• Extend a model to absorb a dataset, reusing conformed dimensions to support cross-domain analysis.
• Replace a legacy report-layer workaround with a well-designed analytical model.
• Contribute a modeling standard, reusable pattern, or documented design for the wider team., * Location: Remote, supporting a Washington, D.C.-based client. Core collaboration hours are 9:00 a.m.-5:00 p.m. Eastern. Eastern and Central schedules are generally most convenient, although applicants across U.S. time zones may be considered.

• Typically 8+ years of professional experience, including 5+ years of data-related production work and 2+ years in analytics engineering, data warehousing, BI engineering, or data engineering, with hands-on design of analytical models that peers or stakeholders used in production.
• Demonstrated dimensional modeling: facts and dimensions at the right grain, conformed dimensions, slowly changing dimensions, and star or snowflake schemas built for analytics.
• Requirements-to-model mapping: experience using a fact-qualifier matrix, fact-dimension or measure-dimension matrix, detailed bus matrix, SSAS Dimension Usage grid, or equivalent working artifact to map business measures to the dimensions and hierarchy levels they are analyzed by.
• Semantic layer experience: semantic models or governed metrics built in tools such as Databricks metric views, Power BI or SSAS Tabular, dbt Semantic Layer, LookML, or comparable tooling.
• Advanced SQL used for complex transformations, analysis, and performance tuning.
• Ability to read and modify Python or PySpark transformation code.
• Experience validating analytical models, including reconciling outputs to source totals and business rules with automated tests.
• Familiarity with source control, code review, and deployment practices appropriate for data assets.
• Ability to learn a business domain, question ambiguous requirements, and explain modeling trade-offs.
• Bachelor’s degree in a relevant field, or equivalent professional experience.

Interviews include a short modeling exercise and a discussion of models you have designed. We prefer walkthroughs of actual models, but we understand that some past work is proprietary or otherwise not shareable. In those cases, we will look for a clear demonstration of the same modeling concepts., No candidate is expected to have all of these.

• Hands-on Databricks experience, especially Spark SQL, PySpark, Delta Lake, Unity Catalog, SQL warehouses, metric views, Lakeflow Spark Declarative Pipelines (formerly Delta Live Tables), or Declarative Automation Bundles (formerly Databricks Asset Bundles).
• Modern analytical transformation tooling such as dbt or SQLMesh.
• Enterprise-scale Kimball practice, such as enterprise bus matrix planning, shared conformed dimensions across subject areas, and handling of ragged or multi-valued hierarchies.
• Designing models for AI/BI consumers, such as Genie Agents or natural-language query tools that depend on well-defined metrics.
• Using a data platform’s built-in coding agent, such as Databricks Genie Code, Snowflake Cortex Code, the BigQuery Data Engineering Agent, or Microsoft Fabric or Synapse Copilot, to build, document, or test analytical models.
• Data governance, row- and column-level security, and lineage, ideally with federal clients or other regulated environments.
• Relevant Databricks, cloud, or data certifications., * Bachelor’s (Required), * hands-on design of production-used analytical models: 2 years (Required)
• data-related production work: 5 years (Required)
• professional: 8 years (Required)

Language:

• Enligh (Required)

• Employment: Full-time salaried. Client delivery represents a standard 40-hour workweek. Digital Strategy expects continuing professional development and knowledge sharing beyond billable client work, with company-supported Databricks lab access, partner training, AI-assisted tool licenses, and case-by-case certification support.
• Travel: Occasional client or team travel, typically once or twice annually.
• Benefits: 401(k), 401(k) matching, dental insurance, health insurance, life insurance, paid time off, and vision insurance.

About Digital Strategy LLC

Digital Strategy LLC is a federal IT and management consulting firm specializing in data modernization, analytics, automation, AI-enabled delivery, and systems integration for U.S. government clients. We combine DOE and federal business-domain expertise with strong technical execution. We value collaborative engineers who improve both data products and team capabilities through reliable delivery, sound judgment, and reusable practices.

Pay: $120,000.00 - $133,000.00 per year, * 401(k)

• 401(k) matching
• Dental insurance
• Health insurance
• Life insurance
• Paid time off
• Vision insurance

Application Question(s):

• This position requires U.S. citizenship, which is different from lawful permanent residence (green card holder) and different from a visa holder. Answer ‘Yes’ to confirm that you are a U.S. citizen, obtained by birth or naturalization.