Senior Data Engineer

Company Overview

Pantheon Data (a Kenific Holding company) is a private, small business based in the Washington, DC, area. Pantheon Data was founded in 2011, initially providing acquisition and supply chain management services to the US Coast Guard. Our service offerings have grown in the past ten years, including infrastructure resiliency, contact center operations, information technology, software engineering, program management, strategic communications, engineering, and cybersecurity.

We have also grown our customer base to include commercial clients. The company has used this experience to expand our service offerings to other agencies within the Department of Homeland Security (DHS), the Department of Defense (DoD), and other Federal Civilian Agencies.

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, 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, 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.

• 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.
• 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.

• Bachelor's degree in Computer Science,Engineering, or a related technical field from an ABET 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, and search-oriented systems.
• 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,…