Data and Analytics Engineer

Data and Analytics Engineer at Digital Strategy LLC supports a U.S. DOE client, designing, delivering, and operating end-to-end data products on a Databricks based cloud platform in a remote environment.

Responsibilities

• Collaborate with architects, analysts, BI developers, team leads, DOE staff, and Digital Strategy leadership to align data products with mission priorities and platform architecture.
• Turn ambiguous business questions into scoped technical designs, delivery roadmaps, and incremental product releases.
• Lead workstreams from intake to delivery, clarifying outcomes, sequencing tasks, and tracking progress.
• Prioritize incoming requests and surface trade-offs among urgency, scope, quality, architecture, and capacity for clients and leadership.
• Present recommendations and constraints clearly to stakeholders with varying technical backgrounds, staying professional when priorities shift or feedback is challenging.
• Apply and continuously refine established architecture and delivery standards, surfacing exceptions and proposed improvements.
• Migrate legacy SQL Server and SSIS workloads to Databricks native pipelines and transformations.
• Develop, schedule, and operate ingestion pipelines for structured, semi-structured, and unstructured sources (relational data, JSON, XML, spreadsheets, documents).
• Architect Bronze, Silver, and Gold data layers with contracts, quality gates, lineage, and tier promotion logic.
• Convert transactional data into BI ready facts, dimensions, semantic structures, and curated data products.
• Write and optimize production grade SQL and Python, including reusable transformations, utilities, and automated tasks.
• Design consumption layer products such as Power BI or Databricks dashboards, lightweight apps, secure APIs, Genie Spaces, and governed NLP analytics experiences.
• Embed data quality checks, validation rules, automated tests, documentation, and operational telemetry into every data product.
• Apply AI and emerging platform capabilities selectively to accelerate delivery and enhance stakeholder access to insights.
• Contribute as a hands-on technical practitioner, making decisions within guardrails and escalating material trade-offs when needed.
• Mentor analysts, BI developers, engineers, and citizen developers in data as code, Git collaboration, modular development, testing, and CI/CD.
• Conduct design and code reviews that explain reasoning and help teammates develop independent judgment.
• Create reusable templates, reference implementations, utilities, and documentation to enable safer, consistent contributions.
• Promote disciplined engineering without over engineering urgent work; choose controls appropriate to risk and audience.
• Identify cross product patterns that can become reusable capabilities, accelerators, or proposal assets.
• Manage metadata, data lineage, ownership, and catalog organization in Unity Catalog.
• Implement least-privilege access controls at row, column, and object levels for all products.
• Coordinate with the DOE Databricks platform team on environment configuration, CI/CD, and promotion across environments.
• Instrument data products with logging, alerts, telemetry, system reports, and data quality monitoring to surface issues early.
• Enforce naming conventions, taxonomy, documentation standards, and release criteria across products and environments.
• Protect sensitive information and comply with federal, client, and platform security requirements.
• Guide data products through design, development, testing, release, and post-production operation with complete evidence before promotion.
• Maintain delivered products for reliability, performance, usability, and cost-effectiveness, tracing issues to root causes.
• Proactively surface gaps, risks, and improvement opportunities and drive resolution with teammates and client staff.
• Improve SOPs, patterns, and delivery practices when experience reveals gaps or friction.
• Balance immediate client value with maintainability to avoid unsupported production dependencies.

Requirements

• 7+ years in data engineering, analytics engineering, business intelligence engineering, or related work, including at least 3 years building, operating, and owning production data products on a cloud analytics platform.
• Bachelor's degree in Data Science, Data Analytics, Computer Science, Engineering, or related field, or equivalent professional experience.
• Proven experience owning data products through design, implementation, testing, release, support, and enhancements.
• Experience leading technical workstreams, articulating trade-offs, reviewing others' work, and mentoring without formal management authority.
• Advanced production-grade SQL across analytical workloads, including complex transformations, tuning, and dimensional 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, translating 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/object level security.
• Ability to build or support analytics consumption products such as dashboards, semantic models, APIs, interactive tools, or natural language data experiences.
• Ability to quickly learn a client's mission and business context to align technical decisions with operations.
• Experience translating complex technical concepts for client stakeholders across backgrounds and co-developing solutions rather than waiting for fully specified instructions.
• Present recommendations and trade-offs with clear rationale, open to challenge, and willing to adjust direction when evidence warrants it.
• Strong stakeholder judgment and professional composure under shifting priorities or high pressure.
• Excellent written and verbal communication, including concise status reporting, technical documentation, and decision records.
• English language proficiency (Required).
• Education and experience aligned with a Bachelor's degree or equivalent professional background.

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

• Become productive within the DOE domain, Databricks environment, and Digital Strategy delivery process within the first months.
• Own at least one data product end to end from intake to production release and enhancement.
• Modernize a legacy pipeline, data model, reporting process, or analytics product.
• Introduce automated quality checks, testing, observability, security, and documentation in assigned products.
• Help team members and citizen developers adopt source control, modular development, code review, automated testing, or CI/CD.
• Turn a recurring data request or manual process into a governed, reusable solution.
• Contribute a reusable pattern, utility, template, accelerator, or lesson that benefits multiple workstreams.

About Digital Strategy LLC

Digital Strategy LLC is a federal IT and management consulting firm focused on data modernization, analytics, automation, AI-enabled delivery, and systems integration for U.S. government clients. We combine DOE and federal domain expertise with strong technical execution and seek collaborative engineers who improve products and teams, deliver reliable client outcomes, and build trusted partnerships through reusable capability.

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, describe 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)