• The DataOps Engineer is responsible for designing, building, and operationalizing data infrastructure that powers the organization's analytics and business intelligence capabilities. • This role sits at the intersection of data engineering, cloud architecture, and DevOps - owning end-to-end data pipelines, platform reliability, data quality, and cloud infrastructure in a regulated environment. • Responsibilities span data pipeline development, cloud migrations, platform operations, and infrastructure management across multi-cloud environments. • Lead the end-to-end migration of data workloads from AWS Commercial to AWS GovCloud, including S3, Glue, Redshift, IAM, VPC, Lambda, and CloudWatch. • Identify and resolve service parity gaps between commercial and GovCloud environments; validate data integrity, encryption posture, and access controls post-migration. • Set up and administer enterprise Git repositories (GitHub, Azure DevOps, or GitLab) for the data and analytics team, including branching strategy, access controls, and code review workflows. • Migrate existing scripts, pipelines, and artifacts into version-controlled repositories and enforce DataOps source control best practices across the team. • Ensure all migrated and provisioned resources comply with NIST 800-171, DFARS, CUI, and ITAR/EAR handling requirements. • Administer and optimize cloud data services across AWS GovCloud (S3, Glue, Redshift, Lambda) and Azure Government (Synapse, ADF, ADLS Gen2, Databricks). • Manage infrastructure-as-code for all data platform resources using Terraform and CloudFormation (required); Ansible preferred for configuration management. • Monitor cloud resource utilization, enforce cost governance, and implement right-sizing recommendations for data workloads. • Maintain backup, disaster recovery, and business continuity procedures for all data platform services. • Serve as Tier 3 support for complex cloud infrastructure and data incidents; perform root cause analysis and implement preventive measures.

• Develop and implement data quality processes across the data engineering lifecycle. • Document, test, validate, and approve developed pipelines and solutions. • Ensure the quality, consistency, and reliability of data delivered to end consumers. • Develop solutions using Python, SQL, and PySpark in a Databricks environment. • Build and maintain notebooks and data processing pipelines. • Participate in requirements analysis with business stakeholders. • Perform unit testing and support solution validation and approval processes. • Research, design, and develop new data engineering solutions. • Continuously improve the reliability, efficiency, and quality of processes and data. • Share technical knowledge and best practices for data engineering and data quality with the team. • Help ensure data generates value for business areas and supports decision-making.

Role Description 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. Qualifications - 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. Requirements - 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. Benefits - Compensation ranges for ASM Research positions vary depending on multiple factors; including but not limited to, location, skill set, level of education, certifications, client requirements, contract-specific affordability, government clearance and investigation level, and years of experience. - The compensation displayed for this role is a general guideline based on these factors and is unique to each role. - Monetary compensation is one component of ASM's overall compensation and benefits package for employees. EEO Requirements - It is the policy of ASM that an individual's race, color, religion, sex, disability, age, sexual orientation or national origin are not and will not be considered in any personnel or management decisions. - We affirm our commitment to these fundamental policies. - All recruiting, hiring, training, and promoting for all job classifications is done without regard to race, color, religion, sex, disability, or age. - All decisions on employment are made to abide by the principle of equal employment. Physical Requirements The physical requirements described in "Knowledge, Skills and Abilities" above are representative of those which must be met by an employee to successfully perform the primary functions of this job. Reasonable accommodations may be made to enable individuals with qualifying disabilities, who are otherwise qualified, to perform the primary functions. Disclaimer The preceding job description has been designed to indicate the general nature and level of work performed by employees within this classification. It is not designed to contain or be interpreted as a comprehensive inventory of all duties, responsibilities and qualifications required of employees assigned to this job.

• Build and maintain the MDM platform, including entity resolution, match/merge rules, survivorship logic, and golden record management. • Develop and enforce data models that support party, policy, and reference data domains across the enterprise. • Architect MDM hub configurations (registry, consolidation, or co-existence models) appropriate to AAIS’s operational context. • Build and maintain MDM integration layers connecting source systems, the data lake/warehouse, and downstream consumers. • Define and implement data governance policies, standards, and workflows in collaboration with Data Stewards and the Director of Data Solutions. • Develop data quality rules, profiling routines, and exception-handling workflows to ensure master data integrity across all jurisdictions. • Maintain business glossaries, data dictionaries, and lineage documentation for all master data domains. • Partner with business stakeholders to define data ownership, stewardship responsibilities, and escalation paths for data quality issues. • Analyze and profile source data to assess quality, completeness, atomicity, and referential integrity prior to MDM onboarding. • Implement automated data quality monitoring and alerting to proactively surface master data anomalies and support data quality KPI tracking. • Establish and track data quality KPIs and SLAs for key master data domains. • Design and develop data integration pipelines that feed the MDM platform from disparate source systems in batch and near-real-time. • Build and maintain ETL/ELT workflows using cloud-native tooling (AWS Glue, Step Functions) and integration platforms. • Ensure referential integrity and consistent application of master data identifiers across the data ecosystem. • Support the broader Data Lake/Warehouse environment, contributing to data modeling, analytics engineering, and BI delivery as needed. • Collaborate with the Data Engineering team to ensure MDM outputs are properly integrated into reporting, analytics, and statistical data collection workflows. • Contribute to Agile sprint planning, technical documentation, and peer code review processes.