OPENDataJobs

Cloud Engineer

• Develop Python applications on Amazon Web Services (AWS)
• Take user stories from refinement through production
• Build serverless, event-driven application workflows using AWS Lambda and Step Functions
• Integrate commercial off-the-shelf (COTS) products with in-house frameworks
• Build internal and external API connections
• Build data and document pipelines that automate manual, paper-based work
• Deliver tested, working code through production, including finding and fixing defects
• Automate business operations that replace manual checks, validations, and spreadsheet workflows
• Build deployment pipelines that make releasing services routine
• Collaborate with developers, engineers, data scientists, architects, and strategists
• Communicate clearly and adapt across roles and clients

Python Developer, AWS, Serverless

• Build Spark-based extract, transform, and load (ETL) pipelines with Glue, Amazon EMR, Lambda, and Step Functions
• Write processing in Python and PySpark
• Design the S3 layer, including Parquet, partitioning, and lifecycle, feeding Apache Iceberg tables
• Connect PostgreSQL on Amazon Aurora and DynamoDB, with Trino for federated SQL across them
• Build ingest, processing, and storage at scale
• Deliver clean and trustworthy data pipelines for high-volume federal platforms
• Automate ingestion previously handled case by case
• Build storage designs supporting downstream systems
• Work alongside developers, engineers, data scientists, architects, and strategists
• Participate in work ranging from strategy to implementation
• Adapt development workflows using AI tools to implement, test, and improve owned components
• Discuss a difficult problem, decisions made, outcomes, and lessons learned

Data Engineer, AWS, Spark

• Own the database layer of a federal agency's enterprise data platform
• Design the physical layer, including partitioning, indexing, and storage layout for large datasets
• Tune queries and execution plans against high volumes
• Build logical and physical data models within the wider platform architecture
• Run schema migrations and Amazon S3 load patterns
• Set Apache Ranger access controls
• Automate migration and load patterns
• Build access controls for a governed platform
• Collaborate with developers, engineers, data scientists, architects, and strategists
• Use AI tools to help implement, test, and improve owned components while reviewing and testing their output
• Contribute to the firm's capabilities, tools, and lines of business across assignments

Senior Database Developer, AWS Data Platforms

• Own assigned delivery pipelines and the environments beneath them
• Build CI/CD pipelines with security gates and controlled promotion
• Define infrastructure as code with reproducible environments and least-privilege access
• Automate security testing, vulnerability scanning, and secrets management across the delivery path
• Build monitoring and evidence supporting Risk Management Framework authorization and continuous monitoring
• Enforce required controls and produce evidence for release reviews
• Automate manual steps out of the release path
• Build progressive delivery with automated health checks and rollback
• Work alongside developers, engineers, data scientists, architects, and strategists on strategy-to-implementation projects
• Adapt development workflows as AI tools evolve, reviewing and testing AI-assisted output and remaining accountable for delivered code

DevSecOps Engineer, AWS Secure Delivery

• Own the technical design and delivery of a defined application or solution area
• Design how application or solution components fit together and guide implementation
• Build cloud-native systems whose automated operation replaces manual processes
• Build and maintain delivery pipelines and automated tests
• Establish development patterns assisted by artificial intelligence tools such as Amazon Q Developer
• Carry integrated solutions through production
• Automate validations and workflows previously managed in spreadsheets
• Improve surrounding developers through code review, explaining reasoning, and setting quality standards
• Choose among competing solution approaches and resolve design tradeoffs across components
• Explain technical work clearly to stakeholders and listen to their needs
• Adapt development workflows as AI tools evolve while reviewing and testing generated output
• Work alongside developers, engineers, data scientists, architects, and strategists across strategy and implementation assignments

Senior Python Developer, AWS Serverless

• Own the queries, stored procedures, views, and logical data models for the assigned part of a federal agency's enterprise data platform
• Build SQL queries, stored procedures, and views in Amazon Aurora (PostgreSQL-compatible)
• Work across DynamoDB, Amazon S3, and Apache Iceberg tables
• Design logical data models and database schemas for assigned components
• Tune queries and build S3 data loads and connectivity under governed access
• Ensure database components are accurate and performant for analysts and systems
• Automate manual data loads and checks
• Build models that keep relational and platform stores working together
• Own assigned database work from design through maintenance
• Implement, test, and improve database components using AI-assisted developer tools while reviewing and remaining accountable for output
• Collaborate with developers, engineers, data scientists, architects, and strategists across strategy and implementation work

Database Developer, AWS Data Platforms

• Build and maintain secure delivery pipelines for routine builds, tests, and deployments
• Add automated security testing, including SAST, DAST, and software composition analysis
• Set up monitoring and logging
• Map controls to NIST SP 800-53
• Investigate and fix security and reliability findings
• Participate in code reviews and explain technical changes
• Automate manual team processes
• Contribute to developing the firm's security-focused engineering practice
• Work alongside engineers and cross-functional teams on federal systems
• Use AI-assisted tools to implement, test, and improve owned components while reviewing and testing outputs