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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer & Modeler (7 Yrs Exp Req) - **Company:** DIGITAL STRATEGIES, LLC - **Location:** United States (Remote available) - **Experience:** Experienced - **Salary:** $100,000.0 - $113,000.0 - **Contract:** Temporary contract - **Skills:** Microsoft Access, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Amazon S3, Business Analytics Applications, Data Analysis, Automation of Tests, Business Intelligence Development, Spreadsheets, Software Documentation, Code Review, Information Systems, Continuous Integration, Data Architecture, Information Engineering, Data Governance, Data Infrastructure, Data Integration, Data Integrity, Data Profiling, Data Structures, Data Warehousing, Relational Databases, Dimensional Modeling, Event Logging, Apache Hive, Identity and Access Management, Intelligence Analysis, JSON, Python (Programming Language), Metadata, Meta-Data Management, Microsoft SQL Server, Modular Design, Networking Basics, Operational Databases, Performance Tuning, Query Optimization, Power BI, SQL Databases, SQL Server Integration Services, Extensible Markup Language (XML), Data Logging, Data Classification, Snowflake, Apache Spark, Model Validation, Amazon Virtual Private Cloud (VPC), Data Layers, Microsoft Fabric, Data Lakes, Pyspark, Information Technology, Data Analytics, Data Management, Software Version Control, Data Pipelines, Databricks - **Published:** September 21, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=a676de1c033fc650 ## About the Role * 7+ years of professional experience in data engineering, analytics engineering, data warehousing, data modeling, or closely related work, including at least 3 years building and materially owning production datasets and data models on an enterprise cloud analytics platform. * Bachelor's degree in Data Science, Data Analytics, Computer Science, Engineering, Information Systems, or a related technical field, or equivalent professional experience. * Demonstrated experience owning data pipelines, analytical data models, and reusable datasets through design, implementation, testing, release, support, and enhancement. * Experience leading technical workstreams, explaining trade-offs, reviewing others' designs or code, and mentoring practitioners without relying on formal management authority. Data Engineering, Modeling & Databricks * Advanced, production-grade SQL across analytical workloads, including complex transformations, query tuning, data profiling, reconciliation, and dimensional modeling. * Production Python or PySpark experience for data engineering, transformation, automation, reusable utilities, or operational tooling. * Strong production experience building and operating ingestion, transformation, and orchestration workflows on Databricks or a comparable enterprise cloud data platform; practical Databricks experience is strongly preferred. * Demonstrated depth in analytical data modeling, including entities and relationships, grain, keys, normalization and denormalization, fact and dimension design, slowly changing dimensions, schema evolution, and semantic modeling. * Experience defining or maintaining reusable semantic models or metric layers, including measures, dimensions, relationships, business terminology, and metadata used consistently across more than one analytical experience. * Experience designing layered lakehouse or warehouse structures, including curated Gold models and semantic structures that translate transactional or operational source data into governed, reusable datasets and metrics for analytics. * Experience with source control, automated testing, code review, CI/CD, and promotion of data-platform assets across environments. * Practical experience with data quality controls, observability, metadata, lineage, and row-, column-, or object-level security. Collaborative Consulting Mindset * Ability to learn a client's mission and business context quickly so technical and modeling decisions reflect operational needs rather than only literal requirements. * Experience translating complex data-engineering, analytical-modeling, and semantic-modeling concepts for client stakeholders across technical backgrounds and co-developing solutions rather than waiting for fully specified instructions. * Presents recommendations and trade-offs with clear rationale, remains open to challenge, and changes direction when evidence warrants it. * Stakeholder judgment and professional composure, especially when priorities shift, delivery pressure rises, or difficult feedback must be communicated. * Strong written and verbal communication, including concise status reporting, technical documentation, model definitions, and decision records. Preferred Qualifications No candidate is expected to possess every preferred qualification. These capabilities indicate areas in which a successful hire may contribute immediately or grow after joining Digital Strategy. Databricks & Lakehouse Depth * Deep practical Databricks experience with capabilities such as Unity Catalog, Delta Lake, Spark SQL/PySpark, Lakeflow Spark Declarative Pipelines, Lakeflow Jobs, SQL warehouses, metric views, system tables, and Declarative Automation Bundles. * Experience implementing medallion architecture and dimensional models at meaningful scale on Databricks, including performance tuning, incremental processing, data quality, and schema evolution. * Experience migrating SQL Server, SSIS, or comparable legacy warehouse workloads and data models to Databricks or another cloud-native lakehouse platform. * Working knowledge of AWS services relevant to an AWS-hosted Databricks deployment, including IAM, S3, VPC, and basic networking concepts. Advanced Analytical & Semantic Modeling * Experience developing conceptual, logical, physical, dimensional, and semantic models across multiple source systems or business domains, including conformed dimensions, reusable measures, shared analytical structures, and governed business terminology. * Experience with declarative data-quality and data-contract frameworks such as pipeline expectations, dbt tests, or comparable tooling. * Experience designing semantic layers or metric models for reuse across multiple downstream experiences, such as Databricks AI/BI dashboards, Apps, Genie Agents, Power BI, self-service analytics, APIs, or AI/ML use cases, while maintaining clear separation between governed analytical meaning and presentation-specific logic. * Experience applying AI to engineering and modeling workflows such as source analysis, data profiling, documentation, metadata enrichment, testing, model review, incident diagnosis, or developer productivity. Federal, Governance & Operations * Prior experience within a U.S. government agency, federal consulting engagement, or other regulated environment. * Experience designing access controls and delivery processes for environments with strong auditability, separation-of-duties, or compliance requirements. * Experience building operational monitoring and alerts from platform system tables, pipeline event logs, or equivalent observability sources. * Experience with enterprise data governance, metadata management, lineage, data classification, or modeling standards across multiple domains or teams., * Bachelor's (Required), * data engineering, data warehousing, or data modeling: 7 years (Required) * analytical or dimensional data modeling: 3 years (Required) * cloud data engineering on enterprise platforms: 3 years (Required) * production Databricks development: 2 years (Preferred) Language: * English (Required) ## Description This is a hands-on engineering and modeling role. The work spans source discovery, ingestion, transformation, integration, analytical modeling, semantic modeling, and production operation across Bronze, Silver, Gold, and semantic layers. The successful engineer will turn transactional and operational source data into well-structured, reusable datasets and models with clear grain, keys, relationships, business rules, metrics, quality controls, and lineage. The primary focus is on pipelines and models, although the role may also build or modify BI and analytics products when needed to validate models, complete delivery, or support urgent client needs. Disciplined engineering provides the structure for this work; AI accelerates it. You will use source control, modular design, automated testing, CI/CD, living documentation, observability, and clear ownership as standard practices, while applying AI-assisted development where it improves coding, analysis, modeling, testing, metadata, documentation, or incident response. AI-generated work must be validated and remain subject to appropriate security controls, engineering review, and human accountability. The position carries notable technical leadership responsibilities and does not require direct personnel management. You will help analysts, engineers, modelers, BI developers, and citizen developers adopt practical data-as-code and modeling practices through design reviews, reusable patterns, code review, automated testing, and environment promotion. The goal is to raise team capability while remaining directly responsible for substantial delivery work. Databricks is the strategic target platform for this engagement, and practical Databricks experience is an important qualification for this role. Strong production experience with Databricks is preferred, particularly with Delta Lake, Spark SQL or PySpark, Lakeflow Jobs and pipelines, Unity Catalog, metric views, and medallion/lakehouse patterns. Candidates with strong directly transferable experience on platforms such as Microsoft Fabric, Snowflake, or comparable cloud analytics platforms may still succeed if they demonstrate deep data-engineering and modeling fundamentals and can become productive quickly in Databricks. Success requires technical depth, analytical modeling judgment, adaptability, consulting judgment, and the ability to rapidly develop DOE mission and business-domain fluency. This is a high-visibility role that communicates directly with federal stakeholders across technical levels and contributes to Digital Strategy's reputation as a trusted advisor through reliable delivery, collaborative problem-solving, and reusable organizational capability. This is a remote position supporting a Washington, D.C.-based client. Most client meetings and team collaboration occur between 9:00 a.m. and 5:00 p.m. Eastern Time. Candidates in all U.S. time zones may be considered, although schedules aligned with Eastern or Central Time are generally the best fit for the engagement. This is a salaried consulting role in which client delivery generally represents a full 40-hour workweek. Employees are also expected to make a reasonable, ongoing investment beyond client-billable hours in professional development, experimentation, certifications, and knowledge sharing so that Digital Strategy remains ahead of evolving client needs and technologies. The role's compensation and responsibilities reflect this broader commitment, and candidates should expect that success will regularly require some additional time beyond 40 hours. Occasional travel is expected, typically one or two times per year, for client meetings or team events. What Success Looks Like During the First Six Months * Become productive within the DOE business domain, Databricks environment, data architecture, modeling standards, and Digital Strategy delivery process. * Own at least one substantial dataset or domain slice from source discovery and model design through production release, operation, and enhancement. * Modernize or materially improve a legacy SQL Server or SSIS pipeline and its associated analytical data structures using Databricks-native patterns. * Design or materially improve reusable Silver, Gold, and semantic models with explicit grain, keys, relationships, business rules, metrics, quality checks, lineage, and documentation. * Strengthen automated testing, observability, security, schema-change handling, and release discipline in assigned pipelines and models. * Contribute a reusable engineering or modeling pattern, utility, template, accelerator, or lesson that improves delivery beyond a single workstream. What You'll Do Consulting, Discovery & Delivery * Partner with architects, data integration analysts, BI developers, product owners, business subject matter experts, DOE staff, and Digital Strategy leadership to align datasets and models with mission priorities and the platform's broader architecture. * Translate ambiguous business questions, source-system behavior, and analytical needs into appropriately scoped data-engineering and modeling designs, delivery plans, and increments. * Drive assigned workstreams from discovery through delivery by clarifying outcomes, sequencing work, managing dependencies and risks, and making technical trade-offs visible to client and Digital Strategy leadership. * Communicate recommendations, constraints, model implications, and technical trade-offs clearly to stakeholders with different technical backgrounds, maintaining professional composure when priorities shift or feedback is difficult. * Apply and continuously improve the team's architecture, engineering, modeling, and delivery standards, constructively surfacing exceptions and proposed refinements. Data Engineering & Analytical Modeling * Migrate legacy SQL Server and SSIS workloads to Databricks-native ingestion, transformation, orchestration, and storage patterns. * Build, schedule, and operate ingestion and transformation pipelines for structured, semi-structured, and unstructured sources, including relational data, JSON, XML, spreadsheets, and documents. * Design and evolve Bronze, Silver, and Gold data structures with clear responsibilities between layers, explicit data contracts, quality gates, lineage, and promotion logic. * Develop conceptual, logical, physical, and semantic data models as appropriate, translating operational source structures and business rules into integrated, analytics-ready structures and reusable analytical meaning. * Design dimensional models with explicit fact grain, dimensions, conformed structures, surrogate and natural keys, slowly changing dimensions, bridge patterns, and other techniques appropriate to the analytical use case. * Make deliberate normalization and denormalization decisions based on business meaning, integration needs, performance, maintainability, data quality, and downstream consumption requirements. * Design for schema evolution, late-arriving or changing source data, incremental processing, referential integrity, reconciliation, and repeatable handling of imperfect source data. * Write and tune production-grade SQL and Python or PySpark, including reusable transformation logic, utilities, performance optimization, and automated operational tasks. * Design, build, and maintain governed semantic models that define reusable measures, dimensions, relationships, hierarchies, terminology, and business metadata for consistent use across analytics products. On Databricks, this may include Unity Catalog metric views and related semantic objects that can be consumed by AI/BI dashboards, Databricks Apps, Genie Agents, external BI tools, and other downstream experiences. * Partner with BI developers and other consumers to ensure Gold and semantic models support Power BI, Databricks AI/BI dashboards, Apps, Genie, APIs, and other downstream uses. Contribute directly to those BI or analytics products when needed, while keeping the role primarily focused on pipelines and models. Technical Leadership & Team Enablement * Serve as a direct technical contributor for assigned datasets and workstreams, making implementation and modeling decisions within established architectural guardrails and escalating material trade-offs when needed. * Conduct constructive data-model, design, and code reviews that explain the reasoning behind recommendations and help teammates develop independent judgment. * Create reusable modeling patterns, reference implementations, utilities, templates, and practical documentation that allow less-experienced contributors to deliver safely and consistently. * Promote disciplined engineering and modeling without over-engineering urgent work; select controls and patterns proportionate to the product's risk, lifespan, complexity, and audience. * Identify patterns across products and engagements that can become reusable Digital Strategy capabilities, accelerators, demonstrations, or proposal assets. Governance, Security & Reliability * Maintain meaningful metadata, lineage, ownership, descriptions, and catalog organization in Unity Catalog for the datasets, data models, and semantic models you build. * Design and implement data structures that support least-privilege row-, column-, and object-level access controls, coordinating with platform administrators and security owners as needed. * Coordinate with the DOE Databricks platform team on environment configuration, CI/CD, access, and promotion dependencies across development, test, and production workspaces. * Instrument pipelines and datasets with logging, alerting, event information, system-table reporting, and data-quality monitoring so issues surface before stakeholders report them. * Apply naming conventions, modeling standards, object taxonomy, documentation standards, and release criteria consistently across products and environments. * Protect sensitive information and comply with applicable federal, client, and platform security requirements. Ownership & Continuous Improvement * Shepherd pipelines, datasets, and data models through design, development, testing, release, and post-production operation, confirming that required evidence and documentation are complete before promotion. * Maintain delivered datasets and models for reliability, performance, usability, and cost-effectiveness, tracing issues through source data, transformations, models, and pipelines to root cause rather than patching downstream symptoms. * Proactively surface unreported data issues, modeling gaps, risks, and improvement opportunities, then collaborate with teammates and client staff to drive them to resolution. * Improve standard operating procedures, engineering patterns, modeling conventions, and delivery practices when experience exposes gaps or unnecessary friction. * Balance immediate client value with maintainability so tactical solutions do not silently become unsupported production dependencies., * [Provide one specific example from your own experience. Address every requested element directly and distinguish your actions from the team's.] Describe one production dataset you personally engineered from source through an analytics-ready model. State the source, pipeline/platform, target data layers, key modeling choices such as grain, keys, or relationships, one source or data-quality problem you encountered, what you personally implemented, and the production result. If you have no comparable example, say so. * [Provide one specific example from your own experience. Address every requested element directly and distinguish your actions from the team's.] Describe one analytical model you personally designed that was reused by more than one downstream report, dashboard, application, or analytical experience. State its fact grain or equivalent core structure, important dimensions or relationships, how reusable metrics or business definitions were represented, and one significant modeling tradeoff you made and why. If you have no comparable example, say so. * [Provide one specific example from your own experience. Address every requested element directly and distinguish your actions from the team's.] Describe one production Databricks workload you personally built or materially changed. Name the Databricks capabilities you actually used, what the workload did, one performance, reliability, or data-quality problem you encountered, exactly what you changed, and the result. 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