Senior Data Engineer - AI/ML Data Pipelines (Remote)

Pantheon-Data; Reston (VA); Hybrid; USD 140,000 - 160,000; Full time

Benefits offered by this job

SmartBenefits program

Transportation benefits

Tuition assistance may be available

Job summary

Pantheon Data seeks a hands-on Data Engineer to design, build, and operate data foundations supporting analytics, AI/ML, and document processing. You will handle data across structured, semi-structured, and unstructured sources with an emphasis on reliability and maintainability.

The role requires strong Python and SQL skills, data modeling, orchestration, and data quality expertise. Collaboration with ML, software, and cloud engineers is essential to deliver production-ready data services for

Qualifications

• 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 with production-oriented code.
• Strong SQL skills with data modeling, performance optimization, joins, indexing, and data quality.
• Understanding of batch processing, event-driven workflows, ETL/ELT, orchestration, idempotency, retries, backfills, lineage, and failure handling.
• Knowledge of OLTP vs OLAP systems and different storage/analysis patterns.
• Experience building/supporting data pipelines moving data between systems (APIs, databases, files, object storage, queues, warehouses).
• Ability to reason about data correctness, schema changes, validation, reconciliation, and operational recovery.
• Proficiency with Git, PRs, code review, documentation, and collaborative practices.
• Strong communication skills to explain data flow and tradeoffs to technical and non-technical stakeholders.
• Ability to work in a distributed, cross-functional environment and meet deadlines.
• Proficiency with Microsoft Office suite.

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.
• Design data models and storage patterns for OLTP, OLAP, object storage, document-oriented, search, vector, or graph patterns where applicable.
• Implement orchestration and scheduling for repeatable data workflows using tools like Airflow, AWS Step Functions, Dagster, Prefect, Glue, or similar.
• Build automated quality checks, reconciliation logic, validation reports, and operational alerts.
• Support data pipelines feeding AI/ML, retrieval, document intelligence, analytics, and application workflows.
• Collaborate with ML 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 velocity of a growing engineering team by owning well-scoped data engineering work while growing into broader system ownership.

Skills

Python

SQL

Data engineering

Batch processing

Orchestration

Data quality

Data modeling

Git

Communication

AWS

Education

Bachelor's degree in Computer Science/Engineering or related technical field

Tools

Airflow

Dagster

Prefect

Glue

AWS services (S3, Lambda, Redshift, etc.)

Spark/Databricks

Job description

Pantheon Data seeks a hands-on Data Engineer to design, build, and operate data foundations supporting analytics, AI/ML, and document processing. You will handle data across structured, semi-structured, and unstructured sources with an emphasis on reliability and maintainability.

The role requires strong Python and SQL skills, data modeling, orchestration, and data quality expertise. Collaboration with ML, software, and cloud engineers is essential to deliver production-ready data services for