Data Architect Engineer

Job Description:

A Senior Data Architect Engineer to support the DIA O&I Enterprise Integration and Assessments Data Team, focused on accelerating dataset understanding and delivering fit-for-purpose machine learning and data solutions.

The role will help the team move from “what data do we have and what can it support?” to “what model or workflow should we implement, and how do we sustain it?” The emphasis is on selecting the right method or tool for the mission problem, including determining when ML is appropriate and when rules, heuristics, or simpler analytics are better suited

Basic Requirements:

B.S. in related field with 5+ years of experience or 10+ years with no B.S.

Must be a U.S. Citizen

Must have an ACTIVE TS/SCI w/ CI Poly Clearance

Senior-level experience supporting machine learning, data science, data architecture, or data engineering efforts

Strong Python and SQL experience

Experience with common machine learning frameworks and libraries, such as scikit-learn, PyTorch, or TensorFlow

Experience assessing datasets for ML readiness, including data quality, metadata, labeling, ground truth, feature engineering, and constraints

Experience designing, building, evaluating, and improving ML models based on mission or business use cases

Experience defining model evaluation metrics and conducting error analysis

Experience building reproducible technical workflows and documenting implementation approaches

Experience using AWS SageMaker for experimentation, training, processing, pipelines, model registry, or deployment approaches

Sound software engineering practices, including Git, readable and modular code, basic testing, documentation, and reproducibility

Ability to work independently, provide technical recommendations, and interface with Government leads and senior stakeholders with limited oversight

Familiarity with scalable data processing tools such as Spark or PySpark is a plus

Responsibilities:

Independently assess and explain datasets, including content, structure, quality, gaps, lineage, metadata, constraints, and known limitations

Identify what is needed to make datasets ML-ready, including labeling, ground truth, feature creation, and data quality considerations

Recommend practical approaches for addressing risk considerations such as bias, drift, and model limitations

Design, build, and iterate ML models appropriate to the data and use case, such as classification, entity or record matching, anomaly detection, and natural language processing, as applicable

Establish baselines and define evaluation metrics tied to operational utility

Perform error analysis to guide model improvements and inform recommendations

Build reproducible workflows that can be rerun and sustained within the customer’s operating environment and security constraints

Support implementation decisions, including batch versus real-time processing, resource and cost tradeoffs, and latency or throughput considerations

Use AWS SageMaker for experimentation and execution, including notebooks or Studio, training jobs, processing jobs, pipelines or automation, model registry, and deployment approaches as permitted

Provide clear technical recommendations to the team and Government lead on data strategy, modeling choices, architecture patterns, and implementation plans

Proactively identify technical risks, opportunities, and recommended paths forward with minimal oversight

ACTIVE TS/SCI with CI POLY CLEARANCE REQUIRED* MUST BE U.S. CITIZEN

Work Location: ON-SITE SUPPORT DIA HQ in Washington D.C.