Senior Data Engineer

Role Description

Simple Technology Solutions is looking for a Senior Data Engineer to join a federal data engineering team. You will design, build, and maintain well-architected ETL pipelines across the full data lifecycle on a cloud-based Enterprise Data Platform (EDP) built on AWS — from data retrieval through ingestion, transformation, semantic layer delivery, and production operations — fully conforming to enterprise ETL standards and governance requirements. You will work at enterprise scale — processing terabytes of financial data across a large portfolio of automated pipelines — as part of an agile team building systems that support critical government functions.

A strong desire for quality, meticulous technical detail, and comfort in a security-conscious federal agile environment are prerequisites for this position. This position is contingent upon contract award.

The Senior Data Engineer at STS will:

• Design, develop, and maintain data retrieval processes for datasets sourced via APIs, SFTP, FTP, web scraping, and Direct Connect from external providers and internal government filing systems — ensuring data is timely, accurate, and stored in the landing zone within contractually required timelines.
• Build and maintain ingestion pipelines using AWS Glue (Spark-based, PySpark), Amazon Managed Workflows for Apache Airflow (MWAA), EMR, Lambda, EventBridge, Step Functions, SQS, and SNS to move datasets from the landing zone through raw and curated zones of the data lake.
• Process and ingest large-volume XML filings from the agency's electronic filing system — parsing structured XML into key-value Iceberg tables using Glue PySpark, supporting batch processing, and handling XML schema evolution for minor changes.
• Store all curated zone data in Apache Iceberg tables using Parquet as the default format (ORC and Avro as justified); support ingestion of CSV, XML, JSON, pipe-delimited, and Avro formats following agency S3 naming conventions and zone-level ingestion rules.
• Prevent duplicate data loads across all target destinations including S3 zones, PostgreSQL, Redshift, Oracle, NoSQL databases, knowledge bases, and vector stores; implement transactional 'All or Nothing' loading and support manual rollback via ETL execution tracking IDs.
• Integrate the agency's ETL Common Library into all Glue jobs for standardized orchestration, error handling, metadata recording, and SNS job notifications; configure static and dynamic ETL metadata in the centralized PostgreSQL metadata store.
• Ensure all ETL jobs automatically populate ETL Load Reports in real-time and ETL Gap Reports on a weekly basis via the metadata infrastructure; support near real-time gap reporting on the operations dashboard.
• Implement technical data quality standards: validate all records and fields are successfully loaded; perform schema validation for applicable filings; implement custom Glue data quality checks (Deequ) as specified by Data Officers and Data Stewards.
• Develop and maintain the semantic layer — including materialized views and Trino/Athena-accessible views — ensuring consistent business logic, optimized query performance, and complete data coverage.
• Deploy ETL resources using CloudFormation templates through the agency CICD pipeline following the established Git branching model; ensure deployments execute with a single command and are submitted to the Change Control Board within required timelines.
• Create and maintain the full documentation suite for each dataset: Business Requirements, ETL Design Document, Data Model (Mermaid format), Data Dictionary, Mapping Document, Deployment Reference and Steps, ETL Operations and Maintenance Guide, ETL Test Plan, and IV&V Questionnaire.
• Support operationalization of statistical outputs and derived data products in coordination with the agency data science team; support ad hoc data requests and transition of ETL jobs from other agency teams.
• Write Python code conforming to PEP 8; achieve minimum 90% automated test coverage; complete security scans at least once per sprint as part of the Definition of Done per OWASP ASVS Level 2.
• Participate in quarterly Program Increment (PI) planning, 2-week sprint ceremonies, backlog refinement, and agile delivery using JIRA and GitHub.

Qualifications

• Bachelor's degree or higher in Computer Science, Information Systems, Data Engineering, or a related field.
• 6+ years of hands-on data engineering experience with strong expertise in AWS Glue (Spark-based), PySpark, and Python (PEP 8).
• Direct experience building large-scale ETL pipelines on AWS using S3, Glue, MWAA (Airflow), EMR, Lambda, SNS, SQS, EventBridge, and Step Functions in a FedRAMP-authorized environment.
• Experience with Apache Iceberg, Parquet, ORC, and Avro file formats and multi-zone data lake architectures.
• Experience with PostgreSQL, Redshift, and Oracle; familiarity with NoSQL, knowledge bases, and vector stores.
• Experience with Trino, Athena, and Hive for semantic layer and query layer development.
• Experience parsing and ingesting large-volume XML datasets with schema evolution handling using PySpark.
• Proficiency with CloudFormation (IaC), GitHub branching workflows, CI/CD pipelines, and AWS Service Catalog-based deployments.
• Demonstrated ability to produce complete ETL documentation: design documents, data models (Mermaid format), data dictionaries, mapping documents, O&M guides, test plans, and IV&V questionnaires.
• Experience with static and dynamic ETL metadata management using PostgreSQL.
• Knowledge of Zero Trust Architecture, FISMA, NIST 800-53, and OWASP ASVS Level 2.
• Experience in agile federal environments with quarterly PI planning and 2-week sprints.
• Familiarity with legacy ETL tools (Hadoop, Perl, Bash) is a plus.
• Must be able to work 8am-5pm Eastern Time regardless of home location.
• Active federal public trust suitability determination or ability to obtain one required.

Benefits

• Flexibility to help team members thrive personally and professionally.
• Special incentives for team members living in qualified HUBZones.