Sr. Analytics Engineer

HYERTEK INC•Rockville, MD

14h•$135,000 - $155,000•Onsite

About The Position

HyerTek is a federal technology consulting firm delivering secure enterprise applications, data analytics, and modernization services to federal government agencies. HyerTek is hiring a Sr. Analytics Engineer to design, build, and own the data and analytics layer that powers our federal solutions. You will model data, build the transformation and scoring logic that turns raw data into trusted metrics and assessments, and deliver the data contracts that reporting and visualization layers render against — partnering with developers, architects, and security teams to deliver secure, performant, well-architected analytics for government customers. This is a hands-on, builder role. On the data-engineering side, you will own data modeling, pipelines, performance, and data quality. On the analytics side, you will build the scoring and derivation logic, the dimensional models, and the governed data products that downstream applications, BI tools, and dashboards depend on. You will work directly with developers, architects, and security engineers, embedded with delivery teams rather than sitting in a separate data organization. Your work will go to federal customers, and the correctness and durability of the data you model will directly shape their trust in HyerTek.

Requirements

5+ years of hands-on data and analytics engineering experience across data modeling, pipelines, and analytics delivery.
Expert SQL, with strong relational and dimensional data modeling.
Demonstrated experience building scoring, rules, or calculation logic over data — not just moving data from one place to another.
Production experience with a major relational database (Azure SQL or SQL Server), including performance tuning.
Proficiency in a backend language for the logic and service layer — C# / .NET preferred; strong Python considered.
Working knowledge of ETL/ELT, data versioning, and effective dating, slowly-changing dimensions, and data-access/security design.
Experience defining data contracts and APIs consumed by separate front-end, reporting, or BI teams.
Excellent troubleshooting, documentation, and stakeholder communication skills.
Comfort working independently across multiple concurrent engagements.
Hands-on experience with Microsoft data and analytics ecosystem tools, including Azure data services, Power Platform, Dataverse, and Microsoft Fabric.
Deep knowledge across data visualization and BI tools — Power BI, Tableau, or comparable — and the judgment to integrate whichever a customer environment requires.

Nice To Haves

Experience with Azure Government, GCC, or GCC High environments and the architectural constraints they impose.
Familiarity with DoD IL4/IL5 workloads or FedRAMP-aligned environments.
MLOps exposure — feature stores, model evaluation, and de-identified/governed data release.
Experience integrating with Microsoft 365, Power Platform, or Dynamics 365 from a data perspective.
Microsoft certifications: DP-203 (Azure Data Engineer), DP-600 (Fabric Analytics Engineer), PL-300 (Power BI Data Analyst), or DP-300 (Database Administrator).

Responsibilities

Design and own relational and dimensional (star-schema) data models across development, test, and production tiers, treating the data model as a long-lived, versioned, peer-reviewed deliverable.
Build the transformation, scoring, and derivation logic that turns raw source data into trusted metrics, assessments, and analytics outputs.
Build and maintain ETL/ELT pipelines and the reference and configuration data behind them, with versioning and effective dating so results stay reproducible over time.
Implement point-in-time and slowly-changing-dimension attribution so historical data rolls up correctly rather than being overwritten by the current state.
Design and tune the analytical store and aggregation strategy for performance, keeping analytics workloads off the transactional systems.
Implement data access controls — row-level and role-based security — aligned to federal security requirements.
Define and maintain the data contracts and APIs that downstream visualization layers consume — native front ends, Power BI, Tableau, or other BI tools — so any of them can render against the same trusted source.
Partner with security engineers to apply data-protection, de-identification, and governed-release controls; remediate findings and support audit activities.
Collaborate with developers and architects to design well-architected data solutions, applying sound modeling and engineering principles.
Document data models, lineage, and as-built records suitable for customer handoff.
Support data quality monitoring, root cause analysis, and continuous improvement across managed environments.
Stay current on the Azure data and analytics ecosystem and bring relevant capabilities into delivery patterns.