Data and Analytics Engineer
ABOUT THE ROLE
Digital Strategy LLC is seeking a Data and Analytics Engineer to design, deliver, and operate end-to-end data products for a U.S. Department of Energy client. The engagement is migrating from an on-premises SQL Server and SSIS data warehouse to a centralized enterprise cloud data platform built on AWS-hosted Databricks, and this role will help modernize the legacy estate while developing for the target platform going forward.
This is a hands-on engineering role that balances rapid-turnaround data requests with the development of governed, reusable analytics solutions. The work may range from an urgent data call one day to a multi-release pipeline, dimensional model, dashboard, natural-language analytics experience, or data application the next. The successful engineer will respond pragmatically to immediate needs while identifying when recurring requests should become tested, documented, and maintainable data products. The mix of rapid-response analysis, data engineering, and analytics-product development will shift with client priorities, but maintainable production data products remain the role's consistent core.
Disciplined engineering provides the structure for this work; AI accelerates it. You will apply source control, modular design, automated testing, CI/CD, living documentation, observability, and clear ownership as standard practices, while using AI-assisted development where it improves coding, analysis, testing, metadata, documentation, or incident response. Use AI-assisted development responsibly, validating outputs and applying appropriate security controls, engineering review, and human accountability.
This is an individual-contributor position with notable technical leadership responsibilities; it does not require direct personnel management. You will help analysts, BI developers, engineers, and citizen developers adopt practical data-as-code techniques, including Git-based collaboration, reusable components, code review, automated testing, and environment promotion. The goal is to establish usable guardrails and build team capability rather than become a bottleneck.
Databricks is the strategic target platform for this engagement, although the role may also maintain and enhance existing SQL Server, SSIS, and Power BI products during the transition. The engineer will balance immediate operational needs with the longer-term modernization roadmap.
Deep Databricks experience is preferred, but engineers with strong, transferable experience on platforms such as Microsoft Fabric, Snowflake, or comparable cloud analytics platforms may also succeed if they demonstrate sound fundamentals and a commitment to learning the Databricks-native toolset.
Success requires technical depth, 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 YEAR
- Within the first several months, become productive within the DOE business domain, Databricks environment, and Digital Strategy delivery process.
- Own at least one data product from intake and design through production release, operation, and enhancement.
- Modernize or materially improve a legacy pipeline, data model, reporting process, or analytics product.
- Introduce or strengthen automated quality checks, testing, observability, security, and documentation in assigned products.
- Demonstrably improve team capability by helping team members and citizen developers adopt source control, modular development, code review, automated testing, or CI/CD practices.
- Convert a recurring data request or manual process into a governed, reusable solution when doing so provides meaningful value.
- Contribute a reusable pattern, utility, template, accelerator, or lesson that improves delivery beyond a single product or workstream.
WHAT YOU'LL DO
Consulting, Intake & Delivery Management
- Partner with architects, analysts, BI developers, team leads, DOE staff, and Digital Strategy leadership to align individual data products with mission priorities and the platform's broader architecture.
- Translate ambiguous business questions and operational needs into appropriately scoped technical designs, delivery plans, and product increments.
- Drive assigned workstreams from intake through delivery by clarifying outcomes, sequencing work, tracking progress, and managing dependencies, risks, and stakeholder expectations.
- Triage incoming requests and make trade-offs among urgency, scope, quality, architecture, and team capacity visible to client and Digital Strategy leadership.
- Communicate recommendations, constraints, and technical trade-offs clearly to stakeholders across technical backgrounds, maintaining professional composure when priorities shift or feedback is difficult.
- Apply and continuously improve the team's established architecture and delivery standards, constructively surfacing exceptions and proposed refinements.
Data Engineering & Product Delivery
- Migrate legacy SQL Server and SSIS workloads to Databricks-native pipeline, orchestration, and transformation patterns.
- Build, schedule, and operate ingestion pipelines for structured, semi-structured, and unstructured sources, including relational data, JSON, XML, spreadsheets, and documents.
- Design Bronze, Silver, and Gold data layers with appropriate contracts, quality gates, lineage, and promotion logic between tiers.
- Translate transactional source data into BI-consumable facts, dimensions, semantic structures, and curated data products.
- Write and tune production-grade SQL and Python, including reusable transformation logic, utilities, performance optimization, and automated operational tasks.
- Design and build consumption-layer products appropriate to the use case, which may include Power BI or Databricks AI/BI dashboards, lightweight Databricks Apps, APIs that securely expose curated data, Genie Spaces, and governed natural-language analytics experiences.
- Embed data quality checks, validation rules, automated tests, documentation, and operational telemetry into every data product as part of normal delivery.
- Use AI and emerging platform capabilities selectively where they improve delivery speed, product usefulness, supportability, or stakeholder access to insights.
Technical Leadership & Team Enablement
- Serve as a hands-on technical contributor for assigned products and workstreams, making implementation decisions within established architectural guardrails and escalating material trade-offs when needed.
- Mentor analysts, BI developers, engineers, and citizen developers as they adopt data-as-code, Git-based collaboration, modular development, automated testing, code review, and CI/CD.
- Conduct constructive design and code reviews that explain the reasoning behind recommendations and help teammates develop independent judgment.
- Create reusable templates, reference implementations, utilities, and practical documentation that allow less-experienced contributors to deliver safely and consistently.
- Promote disciplined engineering without over-engineering urgent work; select controls and patterns that are proportionate to the product's risk, lifespan, and audience.
- Identify patterns across products and engagements that can become reusable Digital Strategy capabilities, accelerators, demonstrations, or proposal assets.
Governance, Security & Reliability
- Manage metadata, data lineage, ownership, and catalog organization in Unity Catalog.
- Implement least-privilege row-, column-, and object-level access controls for the products the team builds and maintains.
- Coordinate with the DOE Databricks platform team on environment configuration, CI/CD, and promotion across development, test, and production workspaces.
- Instrument data products with logging, alerting, event information, system-table reporting, and data quality monitoring so issues surface before stakeholders report them.
- Apply naming conventions, 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 data products through design, development, testing, release, and post-production operation, confirming that required evidence and documentation are complete before promotion.
- Maintain delivered products for reliability, performance, usability, and cost-effectiveness, tracing issues through models and pipelines to the root cause rather than patching downstream symptoms.
- Proactively surface unreported issues, gaps, risks, and improvement opportunities, then collaborate with teammates and client staff to drive them to resolution.
- Improve standard operating procedures, patterns, and delivery practices when experience exposes gaps or unnecessary friction.
- Balance immediate client value with maintainability, ensuring that tactical solutions do not silently become unsupported production dependencies.
REQUIRED QUALIFICATIONS
Core Experience
- 7+ years of professional experience in data engineering, analytics engineering, business intelligence engineering, or closely related work, including at least 3 years building, operating, and materially owning production data products on a cloud analytics platform.
- Bachelor's degree in Data Science, Data Analytics, Computer Science, Engineering, or a related technical field, or equivalent professional experience.
- Demonstrated experience owning data products through design, implementation, testing, release, support, and enhancement.
- Experience leading technical workstreams, explaining trade-offs, reviewing others' work, and mentoring other practitioners without relying on formal management authority.
Data Engineering & Analytics
- Advanced, production-grade SQL across analytical workloads, including complex transformations, query tuning, and dimensional data modeling.
- Production Python experience for data engineering, automation, reusable utilities, APIs, or lightweight data applications.
- Experience building and operating production-grade ingestion, transformation, and orchestration workflows on an enterprise cloud data platform.
- Demonstrated implementation of analytical models, including the translation of transactional schemas into BI-ready facts, dimensions, and curated datasets.
- Hands-on 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.
- Ability to build or support analytics consumption products, such as dashboards, semantic models, APIs, interactive tools, or natural-language data experiences.
Collaborative Consulting Mindset
- Ability to learn a client's mission and business context quickly so technical decisions reflect operational needs rather than only literal requirements.
- Experience translating complex technical 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, 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 & Platform Depth
- Deep hands-on experience with relevant Databricks capabilities, such as Unity Catalog, Lakeflow Spark Declarative Pipelines, Lakeflow Jobs, Delta Lake, Spark SQL/PySpark, Databricks AI/BI dashboards, Genie Spaces, Databricks Apps, Data Quality Monitoring, and Declarative Automation Bundles.
- Experience migrating SQL Server, SSIS, or comparable legacy workloads to a cloud-native data platform.
- Working knowledge of AWS services relevant to an AWS-hosted Databricks deployment, including IAM, S3, VPC, and basic networking concepts.
- Ability to read infrastructure-as-code definitions well enough to request, review, and validate platform changes performed by an administering team.
AI-Enabled Delivery & Advanced Data Products
- Experience integrating large language models, retrieval-augmented generation, or agentic workflows into governed data products.
- Experience building natural-language analytics experiences that translate user questions into reliable, appropriately secured data access or generated code.
- Experience applying AI to engineering workflows such as data quality monitoring, documentation, metadata enrichment, testing, incident diagnosis, or developer productivity.
- Familiarity with preparing and versioning datasets for machine learning training or inference, or orchestrating ML pipelines.
Federal, Quality & Operations
- Prior experience within a U.S. government agency, federal consulting engagement, or other regulated environment.
- Experience with declarative data-quality and data-contract frameworks, such as pipeline expectations, dbt tests, or comparable tooling.
- Experience building operational dashboards and alerts from platform system tables, pipeline event logs, or equivalent observability sources.
- Experience designing access controls and delivery processes for environments with strong auditability, separation-of-duties, or compliance requirements.
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 seek collaborative engineers who improve products and teams, invest in client outcomes, and build trusted partnerships through reliable delivery and reusable capability.
Pay: $100,000.00 - $130,000.00 per year
Benefits:
- 401(k)
- 401(k) matching
- Dental insurance
- Health insurance
- Life insurance
- Paid time off
- Vision insurance
Application Question(s):
- In one to two sentences, identify a production data product you personally owned and your contribution.
- In one to two sentences, describe how you balanced an urgent data request with longer-term engineering work.
- In one to two sentences, identify how you helped a teammate adopt data-as-code, testing, or CI/CD practices.
- In one to two sentences, identify a production data issue you traced to its root cause and prevented from recurring.
- In one to two sentences, identify your role in building a medallion or comparable layered data solution and how data progressed from raw to curated layers.
- Optionally, in one to two sentences, identify one way you have used AI to accelerate data or analytics engineering while maintaining validation and quality controls.
- Can you consistently maintain availability from 9:00 a.m. to 5:00 p.m. Eastern Time? Briefly describe your expected working hours in Eastern Time.
Education:
Experience:
- Databricks production data product development and ownership: 1 year (Preferred)
- Data engineering or analytics engineering: 7 years (Required)
- Cloud analytics platform production data product ownership: 3 years (Required)
Language:
Work Location: Remote
