AI/ML Data Engineer — Secure Data Pipelines & Governance

swingtech-careers; Washington (District of Columbia); On-site; USD 110,000 - 150,000; Full time

Benefits offered by this job

PTO 15 days

Holidays 11

Medical Insurance

Dental Insurance

Vision Insurance

Disability Insurance

Life Insurance

Supplemental Life

Critical Care

Pet Insurance

EAP

401k

Training Budget

Referral Bonus

Annual Bonus

Flexible Work

Job summary

Swingtech is seeking an AI/ML Data Engineer to design and sustain secure data pipelines and governance controls for DOL AI/ML solutions. You will support structured, semi-structured, and unstructured data sources used for analytics, AI/ML development, document intelligence, RAG, and production AI applications.

The role emphasizes ETL/ELT development, data modeling, data quality, and masking/privacy. Onsite presence is required for 3 days at a Washington, DC customer site, with benefits and

Qualifications

• Bachelor’s degree in computer science, data engineering, data science, information systems, software engineering, mathematics, or related technical discipline.
• At least four years of experience in data engineering, database development, analytics engineering, ETL/ELT development, data-platform implementation, or related work.
• Strong SQL and Python development skills.
• Experience designing data pipelines and integrating APIs, databases, file systems, cloud storage, data warehouses, or data lakes.
• Experience with data modeling, metadata, data quality, data lineage, data transformation, monitoring, and operational support.
• Familiarity with AWS, Azure, Google Cloud, or equivalent cloud data services.
• Knowledge of secure data-handling practices, including access control, encryption, data masking, PII protection, and logging.
• Must be willing to work 3 days onsite at customer site in Washington, DC.

Responsibilities

• Design, build, test, deploy, and maintain scalable data pipelines for batch, streaming, near-real-time, and event-driven workloads.
• Integrate approved agency data sources, APIs, file stores, document repositories, relational databases, data lakes, data warehouses, and authorized external sources.
• Develop ETL/ELT pipelines for data extraction, validation, transformation, normalization, enrichment, de-identification, metadata management, and loading.
• Implement document-ingestion pipelines that support OCR, parsing, classification, metadata extraction, PII detection/redaction, chunking, embeddings, vector indexing, and retrieval workflows.
• Create and maintain data models, schemas, data dictionaries, metadata structures, catalog records, and data-quality controls.
• Implement data lineage, source provenance, dataset versioning, retention, access controls, and auditability for training, validation, evaluation, and production datasets.
• Preserve the separation of training, validation, and final evaluation datasets through controlled access, versioning, and documented lifecycle processes.
• Develop and monitor data-quality measures, including completeness, accuracy, timeliness, duplication, validity, freshness, distribution drift, and labeling quality.
• Apply data minimization, masking, encryption, access controls, de-identification, and least-privilege safeguards to PII, CUI, and other protected DOL data.
• Collaborate with AI/ML Engineers to optimize retrieval quality, embeddings, vector stores, hybrid search, reranking, citation traceability, and knowledge-base refresh processes.
• Develop data-pipeline runbooks, technical documentation, source inventories, lineage artifacts, data-quality reports, and operational support procedures.
• Support security, privacy, ATO, Responsible AI, incident response, MLOps, monitoring, and release-readiness activities.

Skills

SQL

Python

Education

Bachelor’s degree in computer science or related field

Tools

AWS

Azure

GCP

OpenSearch

Databricks

Snowflake

Job description

Swingtech is seeking an AI/ML Data Engineer to design and sustain secure data pipelines and governance controls for DOL AI/ML solutions. You will support structured, semi-structured, and unstructured data sources used for analytics, AI/ML development, document intelligence, RAG, and production AI applications.

The role emphasizes ETL/ELT development, data modeling, data quality, and masking/privacy. Onsite presence is required for 3 days at a Washington, DC customer site, with benefits and