Data and Analytics Engineer

Data and Analytics Engineer at Digital Strategy LLC supports a U.S. Department of Energy client by designing, delivering, and operating end-to-end data products on a Databricks based cloud platform. The role blends data engineering, analytics-product development, governance, and AI-assisted delivery in a fully remote setting. Compensation ranges from USD 100,000 – 130,000 per year.

Responsibilities

• Collaborate with architects, analysts, BI developers, team leads, DOE staff, and Digital Strategy leadership to ensure each data product aligns with mission priorities and the broader platform architecture.
• Convert ambiguous business questions and operational needs into well scoped technical designs, delivery plans, and product increments.
• Lead assigned workstreams from intake through delivery by clarifying outcomes, sequencing activities, tracking progress, and managing dependencies, risks, and stakeholder expectations.
• Assess incoming requests and surface trade-offs among urgency, scope, quality, architecture, and team capacity for client and leadership visibility.
• Communicate recommendations, constraints, and technical trade-offs clearly to stakeholders with diverse technical backgrounds while maintaining professionalism when priorities shift.
• Apply and continually improve the team’s architecture and delivery standards, highlighting exceptions and proposing refinements.
• Migrate legacy SQL Server and SSIS workloads to Databricks native pipelines and transformation patterns.
• Design and operate ingestion pipelines for structured, semi-structured, and unstructured sources, including relational data, JSON, XML, spreadsheets, and documents.
• Model Bronze, Silver, and Gold data layers with clear contracts, quality gates, lineage, and promotion logic between tiers.
• Translate transactional source data into BI ready facts, dimensions, semantic structures, and curated data products.
• Write and optimize production grade SQL and Python for reusable transformations, utilities, automation, and operational tasks.
• Design consumption-layer products appropriate to the use case, such as Power BI or Databricks AI/BI dashboards, lightweight Databricks Apps, APIs, Genie Spaces, and governed NLQ experiences.
• Embed data quality checks, validation rules, automated tests, documentation, and operational telemetry into every data product.
• Leverage AI and emerging platform capabilities selectively to speed delivery, improve usefulness, supportability, or stakeholder access to insights.
• Participate as a hands-on technical contributor for assigned products, making implementation decisions within architectural guardrails and escalating material trade-offs as needed.
• Mentor analysts, BI developers, engineers, and citizen developers in data-as-code, Git-based collaboration, modular development, automated testing, code review, and CI/CD.
• Conduct constructive design and code reviews that explain reasoning and help teammates develop independent judgment.
• Create reusable templates, reference implementations, utilities, and practical documentation to enable less-experienced contributors to deliver safely and consistently.
• Promote disciplined engineering without over-engineering urgent work; choose controls and patterns appropriate to risk, lifespan, and audience.
• Identify patterns across products that can become reusable Digital Strategy capabilities, accelerators, demonstrations, or proposal assets.
• Manage metadata, data lineage, ownership, and catalog organization in Unity Catalog.
• Implement least-privilege access controls at the row, column, and object level for built and maintained products.
• 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, alerts, event information, system-table reporting, and data quality monitoring to surface issues proactively.
• 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.
• Guide data products through design, development, testing, release, and post-production operation, ensuring required evidence and documentation are complete before promotion.
• Maintain delivered products for reliability, performance, usability, and cost-effectiveness by tracing issues to root causes rather than patching symptoms.
• Proactively surface gaps, risks, and improvement opportunities and collaborate with teammates and client staff to drive resolution.
• Improve standard operating procedures and delivery practices when experience reveals gaps or friction.
• Balance immediate client value with maintainability to avoid creating fragile production dependencies.

Requirements

• 7+ years of professional experience in data engineering, analytics engineering, or related fields, including at least 3 years 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.
• Proven track record of owning data products from design through release, support, and enhancement.
• Experience leading technical workstreams, articulating trade-offs, reviewing peers, and mentoring without formal management authority.
• Advanced production-grade SQL across analytical workloads, including complex transformations and dimensional modeling.
• Production Python experience for data engineering, automation, 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 and 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 promoting data-platform assets across environments.
• Practical familiarity with data quality controls, observability, metadata, lineage, and security at row, column, or object levels.
• Ability to build or support analytics consumption products such as dashboards, semantic models, APIs, interactive tools, or natural-language data experiences.
• Capability to quickly learn a client’s mission and business context to inform technical decisions beyond literal requirements.
• Experience translating complex concepts for diverse client stakeholders and co-developing solutions rather than awaiting detailed instructions.
• Strong presentation of recommendations and trade-offs with clear reasoning, while staying open to challenge and adjusting direction when warranted by evidence.
• Judgment in stakeholder interactions and professional composure when priorities shift, deadlines tighten, or feedback is challenging.
• Clear written and verbal communication, including concise status updates, technical documentation, and decision records.

Technologies

• Databricks, Unity Catalog, Lakeflow Spark Declarative Pipelines, Lakeflow Jobs
• Delta Lake, Spark SQL, PySpark, SQL Server, SSIS
• Power BI, Genie Spaces, Databricks Apps, Data Quality Monitoring
• Declarative Automation Bundles, AWS, IAM, S3, VPC
• Microsoft Fabric, Snowflake, Git, Python, SQL, dbt
• Databricks AI/BI dashboards

Benefits

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

What success looks like during the first year

• Within the initial months, achieve productive work within the DOE domain, the Databricks environment, and Digital Strategy delivery processes.
• Take ownership of at least one data product from intake and design through production release, operation, and enhancement.
• Modernize or significantly improve a legacy pipeline, data model, reporting process, or analytics product.
• Introduce or reinforce automated quality checks, testing, observability, security, and documentation in assigned products.
• Elevate team capability by guiding team members and citizen developers to adopt source control, modular development, code review, automated testing, or CI/CD practices.
• Turn a recurring data request or manual process into a governed, reusable solution when value is clear.
• Contribute reusable patterns, utilities, templates, accelerators, or lessons that benefit delivery beyond a single product or workstream.

About Digital Strategy LLC

Digital Strategy LLC is a federal IT and management consulting firm focused on data modernization, analytics, automation, and AI enabled delivery for U.S. government clients. The company emphasizes combining DOE and federal domain expertise with strong technical execution and seeks engineers who improve products and teams while delivering reliable, reusable capabilities.

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.

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

• English (Required)