Data Engineer

Groundswell•Washington, DC

10d•$89,886 - $175,444•Hybrid

About The Position

Groundswell is seeking an experienced Data Engineer to build secure, reliable data capabilities for a mission-focused platform that connects authoritative sources, operational data, and analytical services. This role spans data ingestion, integration, quality, governance, and pipeline operations. You will help make complex data usable and trustworthy by designing repeatable workflows that preserve provenance, enforce access controls, and support decision-ready applications and AI-enabled capabilities in controlled environments.

Requirements

Bachelor’s degree in Computer Science, Computer Engineering, Mathematics, Statistics, or a related technical field.
5+ years of professional data engineering experience, including production pipeline development and operations.
Strong SQL expertise, including query optimization, data modeling, joins, window functions, and analysis of large datasets.
Proficiency in Python, Java, R, or other programming language used for data engineering.
Experience with modern data-integration and workflow frameworks such as Apache Airflow, AWS Glue, Spark, or comparable tools.
Experience with Jupyter Notebook or other equvialent tools for analyzing data.
Experience with Python libraries such as numpy, panda, and other libraries like this.
Hands-on experience with at least one major cloud provider, with AWS experience strongly preferred.
Experience implementing data-quality frameworks, validation processes, monitoring, and alerting for production pipelines.
Working knowledge of data security, access controls, encryption, auditability, and governance in a regulated, restricted, or compliance-oriented environment.
Experience working with APIs and structured data formats such as JSON, XML, CSV, and Parquet.
Ability to document technical decisions and collaborate with multidisciplinary teams and customer stakeholders.
Must be a U.S. Citizen per contract requirements.
Must be able to obtain and maintain a Public Trust Clearance in accordance with contract requirements.

Nice To Haves

Experience building preprocessing, chunking, filtering, metadata-enrichment, or evaluation pipelines for LLM and other AI/ML workflows.
Experience operating data platforms with segmented networks, limited connectivity, strict change control, or formal authorization requirements.
Extra consideration given to those with C3.ai experience.
Active Public Trust Clearance.
Preference given to candidates local to the Washington, DC metro area
AWS Data Engineer Certification, Databricks, cloud data engineering, or other relevant professional certification is preferred.

Responsibilities

Design, develop, and operate batch and streaming ETL/ELT pipelines that ingest data from multiple structured and semi-structured sources into secure cloud data platforms.
Onboard new data sources by defining schemas, mappings, interfaces, validation rules, ownership, and operational support procedures.
Build automated data-quality checks for completeness, accuracy, consistency, timeliness, and referential integrity, with actionable monitoring and alerting.
Create data transformations and processing workflows that support operational applications, analytics, machine learning, and retrieval or inference workflows.
Preserve data lineage, provenance, version history, audit trails, and handling metadata throughout the data lifecycle so users can understand where data came from and how it was changed.
Implement secure data practices, including least-privilege access, encryption, secrets management, privacy protections, and controls appropriate for sensitive or classified information.
Develop and maintain data models, curated datasets, and service interfaces that provide consistent, well-documented access to authoritative information.
Automate deployments and pipeline operations using Infrastructure as Code, CI/CD, workflow orchestration, and environment-specific configuration.
Optimize data pipelines for scalability, resiliency, performance, and cost across cloud platforms, including effective partitioning, parallelism, storage, and compute utilization.
Troubleshoot failures across distributed data systems using logs, metrics, lineage, and operational signals; communicate root causes and recovery plans clearly.
Collaborate with software engineers, cloud engineers, data scientists, security teams, architects, and customer stakeholders to translate mission needs into measurable data products.
Produce maintainable data contracts, architecture documentation, runbooks, test plans, and operational guidance for the broader team.
Contribute technical expertise to solution planning and proposal efforts involving secure data platforms and AI-enabled mission capabilities.