Senior Analytics/AI Engineer

Hyertek•Columbia, MD

7d•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 Senior Analytics/AI Engineer to design, build, and own the data, analytics, and AI layer powering our secure federal solutions. You will model data, build the transformation and scoring logic that turns raw records into trusted metrics and assessments, and deliver the deterministic analytics and scope-limited, grounded AI capabilities that decision-makers rely on — partnering with developers, architects, and security engineers to deliver secure, performant, well-architected analytics for government customers on Azure Government Cloud. This is a hands-on, builder role with two halves. On the data-and-analytics side, you will own data modeling, pipelines, performance, and data quality, and build the scoring, derivation, and dimensional logic that produces trusted, reconcilable outputs. On the AI side, you will build a governed natural-language analytics capability on Azure OpenAI — grounded, cited, and strictly scope-limited to what each user is authorized to see — plus predictive and anomaly indicators, with the guardrails that keep them trustworthy in a regulated environment. You will work embedded with delivery teams rather than in a separate data organization, within a continuous-authorization (cATO) pipeline where the correctness of what you build is continuously verified and feeds into the accreditation record. Your work will go to federal customers, and the correctness and durability of the data and models you deliver will directly shape their trust in HyerTek.

Requirements

7+ years of hands-on data and analytics engineering 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 — strong Python, C#/.NET, or Node.js/TypeScript.
Hands-on experience building LLM-backed capabilities — retrieval-augmented generation (RAG), grounding, prompt design, and model evaluation — ideally with Azure OpenAI.
Working knowledge of ETL/ELT, data versioning and effective dating, slowly-changing dimensions, and data-access/security design (row-level and role-based).
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.
Candidates must have and maintain an active TS/SCI clearance with the Department of Defense.

Nice To Haves

Experience with Azure Government Cloud (IL5/IL6/IL7) and the architectural constraints they impose.
Understanding continuous ATO (cATO) / continuous monitoring delivery.
Experience securing AI/LLM systems — scope-bound RAG, PII redaction, prompt-injection defense, and data-residency controls in a governed environment.
MLOps exposure — feature stores, model evaluation, and de-identified/governed data release.
Familiarity with the Microsoft data and analytics ecosystem (Azure data services, Microsoft Fabric) and BI tools (Power BI, Tableau, or comparable), with the judgment to integrate whichever a customer environment requires.

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, reconcilable metrics, assessments, and analytics outputs — where every figure can be re-derived and verified, never hand-entered.
Implement point-in-time and slowly-changing-dimension attribution so historical data rolls up correctly for the period it describes rather than being overwritten by current state.
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.
Design and tune the analytical store and aggregation strategy for performance, keeping analytics workloads off the transactional systems.
Build a scope-based AI query capabilities on Azure OpenAI
Implement AI guardrails and deliver predictive and anomaly-detection indicators as governed, explainable analytics — with evaluation and human-review discipline appropriate to a regulated system.
Implement data-access controls — row-level and role-based security — aligned to federal security requirements, so analytics and AI honor the same scope model the applications enforce.
Define and maintain the data contracts and APIs that downstream visualization layers consume — native front ends, BI tools, or a governed AI layer
Partner with security engineers to apply data-protection, de-identification, and governed-release controls; remediate findings and support continuous-monitoring and audit activities.
Document data models, lineage, model behavior, and as-built records suitable for continuous authorization and customer handoff.