Data Engineer Journeyman

ASM Research•San Antonio, TX

1d•Remote

About The Position

The Data Engineer Journeyman designs, builds, and operates scalable data pipelines and platforms that ingest, process, and store structured and unstructured data to support mission-critical use across the enterprise data environment. Leveraging modern batch and streaming frameworks, this role develops optimized data models, transformation logic, and storage solutions that enable analytics, reporting, and advanced data use cases for business and technical stakeholders. The Data Engineer Journeyman also implements data quality, lineage, and governance practices while ensuring security and compliance in a highly regulated federal data context. This position collaborates with cross-functional teams—including data scientists, analysts, and security stakeholders—to understand data requirements, refine data workflows, and continuously improve platform reliability, performance, and resilience. The engineer troubleshoots pipeline issues, documents data architecture and pipelines, and contributes to ongoing modernization and automation of data engineering processes and tooling across the client environment.

Requirements

Bachelor’s degree in Computer Science, Information Technology, Data Engineering, or a closely related field, or equivalent relevant experience.
Typically 2–5 years of professional experience in data engineering or a closely related field, including hands-on work with data pipelines, data models, and distributed data processing.
Demonstrated experience building and operating batch and/or streaming data pipelines using modern frameworks (e.g., Apache Spark, Kafka) or cloud-native equivalents.
Experience designing and implementing relational and analytical data models, including star, snowflake, and normalized schemas, in support of reporting and analytics.
Practical experience implementing data quality controls, validation, and monitoring, as well as using metadata and catalog tools to manage data assets and lineage.
Demonstrated ability to apply secure data engineering practices, including encryption, access control, and compliance with government or enterprise security standards.
Ability to obtain and maintain a SECRET-level security clearance and work in a U.S.-only staffing context, with U.S. citizenship required.
Willingness and ability to work effectively in a remote, distributed team environment supporting federal or enterprise IT operations.

Nice To Haves

Experience with one or more major cloud platforms (AWS, Azure, or Google Cloud) and their native data engineering services (e.g., AWS Glue, Azure Data Factory, BigQuery, or similar).
Familiarity with data governance frameworks and regulatory compliance requirements applicable to federal or highly regulated environments.
Relevant data engineering or cloud certifications (e.g., AWS Certified Data Analytics, Azure Data Engineer Associate, or equivalent).
Experience automating CI/CD for data pipelines and infrastructure using Terraform, Git-based workflows, and containerization/orchestration technologies.

Responsibilities

Design, develop, and maintain batch and streaming data pipelines using frameworks such as Apache Spark, Kafka, or equivalent cloud-native services to support high-volume ingestion and processing for mission-critical workloads.
Build and optimize data models and schemas (including star, snowflake, and normalized designs) that support analytical, reporting, and operational use cases across the enterprise.
Implement data validation, profiling, and monitoring capabilities to ensure high data quality, integrity, and reliability across all stages of the data pipeline lifecycle.
Design, deploy, and tune scalable storage platforms such as data lakes and data warehouses, employing partitioning, indexing, and compression strategies to improve performance and cost efficiency.
Establish and maintain metadata management and data lineage tracking using catalog and governance tools to provide transparency, auditability, and regulatory compliance for data assets.
Apply secure data engineering practices, including encryption, role-based access controls, and adherence to government data standards and policies in a highly regulated environment.
Automate CI/CD workflows for data pipelines and related infrastructure using tools such as Git, Terraform, and containerization technologies to enable repeatable, reliable deployments.
Troubleshoot and resolve pipeline failures, latency issues, and performance bottlenecks in distributed computing environments, driving root cause analysis and long-term remediation.
Collaborate with data scientists, analysts, and business stakeholders to understand data requirements, refine transformation logic, and ensure that data products meet analytical and operational needs.
Document data architectures, pipeline designs, and operational procedures, and contribute to data governance activities and continuous improvement of data engineering standards and practices.