Senior Data Engineer

Leidos•Gaithersburg, MD

7d•Onsite

About The Position

Join Leidos and help shape the future of geospatial intelligence! We are seeking an experienced and innovative Senior Systems Engineer to support the Maru Program with our Intelligence Community customer. If you’sre passionate about solving complex technical challenges, thrive in a fast-paced Agile environment, and enjoy collaborating with high-performing teams to deliver mission-critical capabilities, this is your opportunity to make a direct impact on national security. The Senior Data Engineer will lead the design and implementation of a scalable, cloud-native data architecture supporting large-scale operational and analytical workloads. This role will provide technical leadership for the development of hybrid analytical query capability, integrating relational databases, object storage, lakehouse technologies, and distributed query engines. The ideal candidate has deep hands-on experience designing modern data platforms and can make architecture decisions around data storage, ingestion, modeling, partitioning, cataloging, governance, and query performance. Please note Gaithersburg, MD is the program’s primary work location. Clearance Level Required: Active Top Secret/SCI with the ability to successfully pass a Polygraph examination.

Requirements

US citizenship is required per contract.
12+ years of relevant software, data engineering, or data architecture experience, with demonstrated progression into senior technical or architecture responsibilities.
Expert-level experience designing and implementing large-scale data architectures, lakehouses, or analytical data platforms.
Strong hands-on experience with SQL and Python.
Strong experience with PostgreSQL and/or Amazon Aurora PostgreSQL.
Experience with Amazon S3 and object-storage-based data architectures.
Hands-on experience with modern lakehouse technologies such as Apache Iceberg, Databricks/Delta Lake, Snowflake, Redshift, or equivalent.
Experience designing hybrid architecture spanning relational, columnar, object-storage, and distributed analytical systems.
Experience with distributed query engines such as Trino/Presto or equivalent technologies.
Experience building batch and/or streaming data pipelines using technologies such as Apache Kafka, Spark, Flink, Airflow, Dagster, dbt, or equivalent.
Strong understanding of data modeling, partitioning, indexing, cataloging, schema evolution, and query optimization.
Experience with Docker/containerized services and modern cloud-native architectures.
Experience with Infrastructure as Code and Git-based CI/CD.
Strong understanding of data security, access controls, governance, and protecting sensitive data.

Nice To Haves

Experience with AWS-managed data services, particularly S3, Aurora PostgreSQL, Redshift, ECS/Fargate, Lambda, and related services.
Experience with Kubernetes and container orchestration.
Experience with data catalogs and metadata platforms such as OpenMetadata, AWS Glue Data Catalog, or equivalent.
Experience working with geospatial, telemetry, operational, or other high-volume datasets.
Experience designing platforms supporting both interactive queries and large-scale analytical workloads.
Experience supporting production data platforms in classified, DoD, Intelligence Community, or other high-security environments.
Experience providing technical directions to multidisciplinary engineering teams.

Responsibilities

Lead the architecture and implementation of a hybrid analytical query platform supporting large-scale operational and analytical data.
Design and implement modern data lakehouse architectures using object storage, relational databases, and distributed analytical technologies.
Develop scalable batch and streaming data pipelines for ingestion, transformation, enrichment, and delivery of data.
Design data models, partitioning strategies, indexing approaches, catalogs, and query architectures optimized for large datasets.
Integrate PostgreSQL/Aurora transactional data with analytical and object-storage platforms.
Design and optimize distributed query capabilities using technologies such as Trino/Presto or equivalent.
Develop solutions using Amazon S3, Apache Iceberg, Redshift, and other modern cloud data services.
Support data governance, metadata management, lineage, access controls, and security requirements.
Design and deploy containerized data services within cloud-native environments.
Provide technical leadership, architecture guidance, design reviews, and mentoring engineers to implementing the data platform.
Troubleshoot complex data, database, query-performance, infrastructure, and integration issues.