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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data & Analytics Engineer (Remote, 7+ Yrs Exp Req) - **Company:** DIGITAL STRATEGIES, LLC - **Location:** United States (Remote available) - **Experience:** Expert - **Salary:** $120,000.0 - $130,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Amazon S3, Automation of Tests, Business Intelligence Development, Code Review, Information Systems, Continuous Integration, Information Engineering, Data Profiling, Data Structures, Data Warehousing, Digital Assets, Dimensional Modeling, Identity and Access Management, Python (Programming Language), Microsoft SQL Server, Networking Basics, Operational Databases, Query Optimization, Power BI, Search Technologies, SQL Databases, SQL Server Integration Services, Enterprise Data Management, Data Logging, Sql Optimization, Large Language Models, Apache Spark, Generative AI, Amazon Virtual Private Cloud (VPC), Data Layers, Pyspark, Information Technology, Data Analytics, Data Management, Machine Learning Operations, Software Version Control, Databricks - **Published:** October 2, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=43bafefbc2cd20f8 ## About the Role * 7+ years of experience in data engineering, analytics engineering, data warehousing, or data modeling, including at least 3 years building and owning production datasets and models on an enterprise cloud analytics platform. * Bachelor's degree in Data Science, Computer Science, Engineering, Information Systems, or a related technical field, or equivalent professional experience. * At least 12 months of hands-on production experience with Databricks, measured in working time actually spent in the platform, not calendar time since you first used it. For example, two years on a project where Databricks was about a quarter of your work counts as roughly six months. Experience may span multiple projects. Please describe this experience in your résumé or application so we can recognize it. * Advanced SQL and production Python or PySpark, including complex transformations, query tuning, data profiling, and reconciliation. * Deep analytical modeling skills, including grain, keys, fact and dimension design, slowly changing dimensions, normalization trade-offs, and semantic models or metric layers reused across more than one analytics experience. * Experience with source control, automated testing, code review, and CI/CD for data assets, along with data quality controls, lineage, and row-, column-, or object-level security. * Technical leadership without formal authority, including leading workstreams, reviewing others' designs and code, and mentoring practitioners. * Consulting judgment: learns a client's mission quickly, explains trade-offs with clear rationale to stakeholders across technical backgrounds, stays open to challenge, and remains composed when priorities shift., * No candidate is expected to have every preferred qualification. * Experience building AI capabilities on Databricks, such as Genie Agents, Knowledge Assistants, Databricks Apps that use LLMs, AI Functions, Vector Search, or Model Serving, including evaluating output quality. * Regular use of Genie Code or other AI-assisted tools across engineering and modeling work. * Databricks depth beyond the required baseline, such as Lakeflow Spark Declarative Pipelines (formerly Delta Live Tables), SQL warehouses, metric views, system tables, or Declarative Automation Bundles (formerly Databricks Asset Bundles). * Experience migrating SQL Server, SSIS, or comparable legacy warehouse workloads to Databricks or another cloud-native lakehouse platform. * Working knowledge of AWS services relevant to an AWS-hosted Databricks deployment, such as IAM, S3, VPC, and basic networking. * Experience with declarative data-quality or data-contract frameworks, and monitoring built from system tables or pipeline event logs. * Prior experience with a U.S. government agency, federal consulting engagement, or other regulated environment with strong auditability or separation-of-duties requirements. * Databricks Certified Data Engineer Associate or Professional, Databricks Certified Generative AI Engineer Associate, or a relevant AWS certification., * Bachelor's (Required), * data engineering (actual hands-on working time): 7 years (Required) * analytical/dimensional modeling (actual working time): 3 years (Required) * Databricks development (actual hands-on working time): 1 year (Preferred) Language: * English (Required) ## Description * The engagement: Digital Strategy LLC is seeking a Data & Analytics Engineer to build and operate production data pipelines, analytical models, and AI-enabled data products for the U.S. Department of Energy's Office of Critical Minerals and Energy Innovation (CMEI). The office is migrating from an on-premises SQL Server and SSIS data warehouse to an enterprise data platform built on AWS-hosted Databricks. * The work: Hands-on engineering and modeling across ingestion, transformation, dimensional and semantic modeling, and production operation in the Bronze, Silver, Gold, and semantic layers. The work extends into AI on Databricks, including Genie Agents, Knowledge Assistants, and Databricks Apps that use large language models (LLMs). * The team: You will collaborate with other Digital Strategy and client data engineers/modelers, architects, BI developers, product owners and managers, and business SMEs. Because Digital Strategy keeps its teams lean, you will also work directly with federal stakeholders and firm leadership. * Leadership: This is a senior individual contributor role with technical leadership responsibilities and no direct reports. You will raise team capability through design reviews, code reviews, reusable patterns, and mentoring while remaining directly responsible for substantial delivery. * How we operate: We are actively strengthening our engineering discipline, including source control, automated testing, CI/CD, living documentation, and observability, and you will help shape how these practices take hold. AI-assisted development with tools such as Genie Code is expected. AI-generated work is validated and remains subject to engineering review, security controls, and human accountability. * Platform: At least 12 months of hands-on production Databricks experience is required, measured in working time actually spent in the platform rather than calendar time since first use. Experience with Databricks AI capabilities is preferred rather than required but expect it to be a significant and growing part of your work with this client. Work Arrangement * Remote, supporting a Washington, D.C.-based client. Core collaboration hours are 9:00 a.m. to 5:00 p.m. Eastern Time. Eastern or Central Time schedules are generally the best fit, though candidates in all U.S. time zones may be considered. * Full-time salaried role, with client delivery representing a standard 40-hour workweek. Digital Strategy expects consultants to invest a few hours per week beyond billable hours in certifications, experimentation, and knowledge sharing, and the company supports this with a Databricks lab environment, Databricks partner training, AI-assisted tool licenses, and case-by-case payment for relevant certifications. * Occasional travel, typically once or twice a year, for client meetings or team events. What Success Looks Like in the First Six Months * Become productive in CMEI's mission domain, the client's Databricks environment, and the team's architecture, modeling standards, and delivery practices. * Own at least one substantial dataset or domain slice from source discovery and model design through production release and operation. * Modernize a legacy SQL Server or SSIS pipeline and its data structures using Databricks-native patterns. * Design or materially improve reusable Gold and semantic models with documented grain, keys, business rules, quality checks, and lineage. * Deliver or materially contribute to at least one AI-enabled capability, such as a Genie Agent built on governed semantic models or a Knowledge Assistant over program documents. * Contribute a reusable pattern, template, or accelerator that improves delivery beyond your own workstream. What You'll Do Data Engineering and Modeling * Migrate legacy SQL Server and SSIS workloads to Databricks-native ingestion, transformation, orchestration, and storage patterns. * Build and operate pipelines for structured, semi-structured, and unstructured sources, handling schema changes, late-arriving data, incremental processing, and reconciliation in a repeatable way. * Design Bronze, Silver, and Gold structures and dimensional models with clear layer responsibilities, explicit fact grain, conformed dimensions, surrogate and natural keys, and slowly changing dimensions. * Design governed semantic models, including Unity Catalog metric views, that define reusable measures and dimensions for Power BI, AI/BI dashboards, Genie, and Databricks Apps, contributing directly to BI products when needed. AI on Databricks * Build Genie Agents on governed Gold and semantic models so client staff can ask questions of trusted data in natural language, curating the instructions, example queries, and metadata that keep answers accurate. * Develop Knowledge Assistants that ground responses in program documents and other unstructured sources, with access controls that respect data sensitivity. * Build Databricks Apps that use LLMs to deliver task-focused tools for client users, and apply capabilities such as AI Functions in pipelines where they add value. * Evaluate and monitor AI capabilities for accuracy and appropriate use, and help the team adopt Genie Code and other AI-assisted practices responsibly. Technical Leadership and Collaboration * Partner with other data engineers, as well as architects, BI developers, product owners, and CMEI staff to turn ambiguous business questions into well-scoped designs and delivery increments. * Make implementation and modeling decisions within architectural guardrails, making trade-offs visible to the team and client stakeholders. * Lead design and code reviews that explain the reasoning behind recommendations, and create reusable patterns, templates, and documentation that help others deliver consistently. Governance, Reliability, and Ownership * Maintain metadata, lineage, ownership, and descriptions in Unity Catalog, and apply the team's naming, modeling, documentation, and release standards. * Implement row-, column-, and object-level access controls with platform administrators and security owners, protecting sensitive information under applicable federal, client, and platform requirements. * Coordinate with the DOE Databricks platform team on environment configuration, CI/CD, and promotion across development, test, and production workspaces. * Instrument pipelines with logging, alerting, and data-quality monitoring, and trace issues to root cause rather than patching downstream symptoms. * Balance immediate client value with maintainability, choosing controls proportionate to a product's risk, lifespan, and audience so tactical solutions do not quietly become unsupported production dependencies.