Data Engineer (AI-ML Data Systems)

Position Overview

We are seeking a hands-on Data Engineer to help design, build, and operate the data foundations that support advanced analytics, AI/ML, and intelligent document processing solutions. The right candidate is a strong engineer who understands how data moves through real systems: ingestion, orchestration, transformation, quality checks, storage, query patterns, operational monitoring, and delivery to downstream applications. This person should be comfortable working across structured, semi-structured, and unstructured data held in relational, graph, and vector data stores, and should bring the judgment to build pipelines that are reliable, explainable, maintainable, and useful to the engineering teams and products that depend on them.

The ideal candidate has strong Python and SQL skills, understands when data should be modeled for operational use versus analytical use, and can reason clearly about batch processing, event-driven pipelines, data quality, lineage, and downstream consumption. They should be able to become productive quickly in a complex engineering environment, ask good questions, and build systems that other engineers can trust and extend. Experience with AWS, graph databases and query languages such as AQL, vector search, document data, or AI/ML data pipelines is valuable, but the core requirement is strong data engineering judgment: knowing how to move, structure, validate, and serve data reliably in support of real products and mission needs.

This role owns the data pipelines and data stores. You will partner closely with the Machine Learning Engineers, who own models, retrieval, extraction, and evaluation: they define how documents are chunked, embedded, enriched, and retrieved, and you build and run the pipelines and stores that apply those designs reliably at scale.

Responsibilities

• Design, build, and maintain reliable data pipelines for structured, semi-structured, and unstructured data sources.
• Develop Python-based processing workflows for data ingestion, normalization, validation, enrichment, and transformation.
• Work with SQL and relational data stores to support transactional, analytical, and application-facing use cases.
• Work with graph databases and query languages such as ArangoDB/AQL to model relationships between entities, documents, and metadata, and to support traversal and relationship-driven queries.
• Build and operate vector data pipelines and stores: run embedding generation at scale; manage vector index builds, refreshes, backfills, and re-embedding when models change; and serve the lookups that retrieval and RAG workloads depend on, implementing the chunking, embedding, and metadata specifications defined by the Machine Learning Engineers.
• Help design data models and storage patterns appropriate to the workload, including OLTP, OLAP, object storage, document-oriented, search, vector, or graph-oriented patterns when applicable.
• Implement orchestration and scheduling for repeatable data workflows using tools such as Airflow, AWS Step Functions, Dagster, Prefect, Glue workflows, or similar technologies.
• Build automated quality checks, reconciliation logic, validation reports, and operational alerts so data issues are detected early and can be diagnosed quickly.
• Support data pipelines that feed AI/ML, retrieval, document intelligence, analytics, and application workflows.
• Collaborate with machine learning engineers, software engineers, cloud engineers, and product stakeholders to turn ambiguous data problems into working software.
• Write maintainable code, participate in code reviews, document data flows, and contribute to engineering standards for testing, deployment, observability, and version control.
• Help improve the velocity of a growing engineering team by taking ownership of well-scoped data engineering work while continuing to grow into broader system ownership.

Required Skills and Experience

• Bachelor's degree in Computer Science, Engineering, or a related technical field from an accredited university.
• 5+ years of professional hands-on data engineering, software engineering, analytics engineering, or closely related experience.
• Strong Python programming skills, including experience writing maintainable production-oriented code rather than only notebooks or one-off scripts.
• Strong SQL skills and practical understanding of data modeling, query performance, joins, indexing, schemas, normalization/denormalization, and data quality.
• Understanding of core data engineering concepts, including batch processing, event-driven workflows, ETL/ELT, orchestration, idempotency, retries, backfills, lineage, and failure handling.
• Working knowledge of OLTP versus OLAP systems and the tradeoffs between transactional databases, analytical stores, object storage, search-oriented systems, and graph and vector databases.
• Experience building or supporting data pipelines that move data between systems, such as APIs, databases, files, object storage, queues, warehouses, or downstream applications.
• Ability to reason about data correctness, schema changes, validation, reconciliation, duplicate handling, missing data, and operational recovery.
• Comfortable working with Git, pull requests, code review, issue tracking, documentation, and collaborative software development practices.
• Strong communication skills and the ability to explain data flow, design choices, limitations, and tradeoffs to both technical and non-technical stakeholders.
• Ability to work effectively in a distributed, cross-functional engineering environment and produce high-quality work with limited hand-holding.
• Ability to work effectively remotely in cross-functional teams.
• Ability to meet deadlines and produce quality work.
• Proficient in Microsoft Suite software including Outlook, Word, Excel, SharePoint, and PowerPoint.

Preferred Skills and Experience

• AWS data services such as S3, Lambda, Glue, Athena, Step Functions, SQS/SNS, Kinesis, EMR, RDS, DynamoDB, Redshift, OpenSearch, or CloudWatch.
• Experience with workflow orchestration tools such as Airflow, Dagster, Prefect, AWS Step Functions, Glue, or similar.
• Experience with PySpark, Spark, Databricks, EMR, Snowflake, Redshift, or other distributed/analytical data platforms.
• Experience supporting AI/ML or RAG-style data workflows, including metadata enrichment, retrieval datasets, vector search, embeddings, evaluation datasets, or human validation workflows.
• Experience with document-oriented or unstructured data pipelines, including PDFs, OCR output, tables, forms, images, extracted text, metadata, or search indexes.
• Hands-on experience with graph databases and query languages such as ArangoDB/AQL, Neo4j/Cypher, or Gremlin, including graph-shaped data modeling.
• Experience with vector databases or vector search engines such as pgvector, OpenSearch, or similar, including index tuning and hybrid (vector + lexical) lookups.
• Experience with Docker, CI/CD, infrastructure as code, automated testing, logging, monitoring, and production support is a plus.
• Familiarity with data governance, access control, PII handling, auditability, lineage, and compliance-sensitive environments.

Clearance Requirements

Applicants selected will be subject to a security investigation and may need to meet eligibility requirements. Candidates must have the ability to obtain and maintain a Secret clearance; an active Secret clearance is preferred.

Work Location: Reston, VA - Remote

• Our company prioritizes the benefits of flexibility and collaboration, whether that happens in person or remotely.
• If the position is remote or hybrid, you may periodically work from a Pantheon Data office location or client site.
• If this position is assigned to a Pantheon Data office location or client site, you'll work with colleagues and clients in person, as needed for specific client requirements.

Interview Requirement: Candidates who are local to the area should be prepared to participate in an in-person interview as part of the selection process. Candidates outside the local area may be considered for a virtual interview.

Compensation

The salary range for this position is $110,000 - $190,000. This is not, however, a guarantee of compensation or salary. Rather, salary will be set based on experience, geographic location and possibly contractual requirements and could fall outside of this range.

Benefits Overview

We are always looking for good people! Pantheon Data is committed to providing its employees with competitive salaries and benefits in order to increase employee satisfaction and productivity. In addition to our benefits, we also offer SmartBenefits through the Washington Metro Area Transportation Authority, where you specify an amount of your pre-tax wages be paid directly to your SmarTrip account. In some cases, tuition assistance may be available for continuing education expenses and certifications related to their position. Additional details may be found at