Data Engineer - Level III

AttainX; Fort Collins (CO); Remote; USD 120,000 - 135,000; Full time

Job summary

AttainX is seeking a Data Architect / Data Engineering Lead for a remote 3-4 month assignment under USDA OMNI for NRCS Operations & Maintenance. The role focuses on data architecture, modernization of monolithic SQL Server environments, and cloud-native AWS solutions to support data management and AI/ML enablement.

The candidate will guide legacy-to-cloud migrations, design enterprise data architectures (warehouses, lakes, lakehouses), and drive end-to-end data pipelines with AWS services, while

Qualifications

• 7 years of progressive experience in data architecture, data engineering, and database administration.

Responsibilities

• Define and maintain data architecture standards, patterns, and governance practices across NRCS systems.

Skills

Data architecture

Data engineering

SQL Server

PostgreSQL

Python

PySpark

AWS

ETL pipelines

Geospatial data

Bash/PowerShell

Jira

Education

Master's degree in Computer Science / Data Science or related field

Tools

Terraform

CloudFormation

Liquibase

Git

Databricks

DMS

OpenSearch

Lambda

Step Functions

RDS/Aurora PostgreSQL

Job description

Job Title : Data Architect / Data Engineering Lead

Location : Remote - Temporary 3-4 Month Assignment

Contract : USDA OMNI - NRCS Operations & Maintenance

Security Clearance : USDA Public Trust

Salary Range: $120,000.00 - $135,000.00 per year.

The Data Architect / Data Engineering Lead provides technical leadership for data architecture, data engineering, database modernization, and AI/ML enablement across the NRCS IT ecosystem. This role is responsible for guiding the transformation of legacy data platforms - including monolithic SQL Server environments, SSIS-based ETL pipelines, and tightly coupled cross-database dependencies - into scalable, cloud-native architectures on AWS. The position works in close coordination with the Enterprise Lead Architect, Government Program Managers, and cross-functional delivery teams to execute data management, modernization, and operational sustainment activities under the OMNI contract.

Required Qualifications:

• Experience: 7 years of progressive experience in data architecture, data engineering, and database administration across enterprise environments.
• Cloud Platforms: 5+ years of hands-on experience designing and deploying data solutions on AWS, including direct experience with S3, Glue, EMR/Spark, Lambda, Step Functions, DMS, RDS (PostgreSQL, Aurora), DynamoDB, OpenSearch, and Lake Formation.
• Database Technologies: Deep expertise in Microsoft SQL Server and PostgreSQL/PostGIS. Experience with database decoupling and monolithic database decomposition.
• Data Engineering: Proven experience building production data pipelines for batch, streaming, and geospatial workloads. Experience modernizing legacy ETL (SSIS) to cloud-native frameworks.
• Programming: Strong proficiency in SQL/T-SQL, Python, and PySpark. Working knowledge of Bash/PowerShell for automation.
• Architecture: Demonstrated ability to design and implement enterprise data architectures including data warehouses, data lakes, lakehouses (Delta Lake), and service-layer integration patterns.
• Federal IT: 3+ years of experience supporting federal IT programs, with familiarity with FISMA, NIST RMF, ATO processes, and federal change management requirements.
• DevSecOps: Experience with CI/CD pipelines, Git-based version control, Terraform or CloudFormation, Liquibase, and automated quality/security gates.
• Agile/SAFe: Experience working within SAFe Agile or equivalent iterative delivery frameworks, including backlog management in Jira.

Preferred Qualifications:

• Direct experience with USDA systems
• Experience with FPAC IT governance, the Technical Guidance Framework (TGF), and FPAC CI/CD pipeline standards.
• Hands-on experience with AWS Bedrock, SageMaker, and Generative AI patterns (RAG, embeddings, natural-language-to-SQL, LangChain).
• Experience with geospatial data engineering, including PostGIS, GeoPackage, ArcGIS WFS/WMS services, and spatial data pipelines.
• Experience with AI-enabled legacy modernization platforms (e.g., Rhino.ai or equivalent).
• Azure experience (Synapse, ADF, ADLS, Azure ML Studio, Databricks on Azure) as a complement to primary AWS focus.
• Relevant certifications: AWS Solutions Architect, AWS Data Analytics Specialty, Azure Data Engineer Associate (DP-203), or equivalent.
• Master's degree in Computer Science, Data Science, or related filed.

Responsibilities:

• Data Architecture and Strategy: Define and maintain data architecture standards, patterns, and governance practices across all NRCS systems, ensuring alignment with FPAC's Technical Guidance Framework (TGF), Cloud Memo directives, and Zero Trust principles.
• Lead conceptual and logical decomposition of monolithic database structures (e.g., NPAD) into domain-aligned, modular schemas that support incremental modernization and cloud migration.
• Architect service-layer data access patterns to replace direct cross-database queries and business logic embedded in stored procedures, reducing architectural fragility and enabling decoupled deployments.
• Design and maintain data models for enterprise soil data systems including NASIS, Soil Data Warehouse (SDW), Soil Data Marts (SDM), and related spatial/tabular datasets.
• Align supported systems with USDA's cloud-native Lakehouse Data Strategy, including adoption of Databricks as the departmental standard data integration tool and elimination of duplicated data copies.
• Register and maintain schemas, interfaces, and metadata in AWS DataZone (or Government-directed metadata tooling), ensuring synchronization across environments.
• Data Engineering and Pipeline Development: Design, build, and maintain end-to-end data engineering pipelines using AWS-native services (Glue, EMR/Spark, Lambda, Step Functions, EventBridge, DMS, S3, RDS/Aurora PostgreSQL) for batch, streaming, geospatial, and near-real-time workloads.
• Modernize legacy SSIS-based ETL/ELT pipelines to cloud-native equivalents (AWS Glue, Databricks, PySpark), improving scalability, maintainability, and operational efficiency.
• Build and operate AWS DMS full-load a