Geospatial Data Architect (Hybrid Eligible)

Oak Ridge National Laboratory•Oak Ridge, TN

5d•Hybrid

About The Position

We are seeking a Geospatial Data Architect who will focus on designing and governing data ecosystems that support spatial analytics, modeling, and operational workflows. This position resides in the Geoinformatics Engineering Group in the Human Dynamics Section, Geospatial Science and Engineering Division, National Security Sciences Directorate at Oak Ridge National Laboratory (ORNL). As a U.S. Department of Energy (DOE) Office of Science national laboratory, ORNL has an impressive 80-year legacy of addressing the nation’s most pressing challenges. Our team is made up of over 7,000 dedicated and innovative individuals! Our goal is to create an environment where a variety of perspectives and backgrounds are valued, ensuring ORNL is known as a top choice for employment. These principles are essential for supporting our broader mission to drive scientific breakthroughs and translate them into solutions for energy, environmental, and security challenges facing the nation.

Requirements

A BS or MS degree in Computer Science, Geospatial Science, Data Engineering, or related field
A minimum of 5 years of experience in data architecture, engineering, or backend systems
Strong understanding of data modeling (relational and non-relational)
Experience with SQL and at least one major database system
Familiarity with geospatial data types and tools (vector/raster concepts, GDAL, spatial indexing basics)

Nice To Haves

Experience with Parquet and GeoParquet, including hive-partitioning, partitioning strategies, compression, and query optimization in object storage environments
Hands-on experience designing data systems on S3 or S3-compatible object storage platforms
Design and operate modern geospatial data platforms, with a strong understanding of data lake/lakehouse/warehouse tradeoffs and governance (access control, policy enforcement, lifecycle management, etc.).
Build scalable processing pipelines for large raster/vector datasets using distributed compute and spatial analytics tooling (e.g., Spark/Dask/Flink, Sedona/BigQuery GIS, etc.)
Work with cloud-native geospatial formats and core spatial concepts (CRSs, transformations, spatial indexing such as H3/S2, etc.).
Implement data management best practices across the lifecycle: versioning and reproducibility, metadata/catalogs and lineage, and reliable transfer/sharing (e.g., DVC-like tooling, STAC-like standards, DataHub/Amundsen, Globus/iRODS, etc.)
Motivated self-starter with the ability to work independently and to participate creatively in collaborative teams across the laboratory
Ability to function well in a fast-paced research environment, set priorities to accomplish multiple tasks within deadlines, and adapt to ever changing needs

Responsibilities

Design and govern geospatial data architecture: Define scalable data models, storage patterns (e.g., Parquet/GeoParquet in object stores), and overall lakehouse/warehouse strategy to support spatial analytics and interoperability.
Establish data standards, integration patterns, and lifecycle management: Oversee ingestion frameworks, metadata/cataloging (e.g., STAC), data versioning (e.g., DVC), and policy-driven management systems (e.g., iRODS, Globus), ensuring consistency, reproducibility, and accessibility.
Optimize performance, scalability, and cross-team alignment: Implement efficient partitioning, indexing, and distributed processing strategies while coordinating with engineers and scientists to align infrastructure with geospatial workflows and analytical needs.
Deliver ORNL’s mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. Promote equal opportunity by fostering a respectful workplace – in how we treat one another, work together, and measure success.