Data Engineer

TL;DR

Data Engineer (ETL/AWS): Design, build, and maintain scalable ETL and data sourcing pipelines in an AWS-based enterprise data platform with an accent on data quality, performance, security, and conformance to enterprise ETL standards. Focus on reliable ingestion from external and internal sources, production-grade orchestration, and preparing datasets to be AI-ready for advanced analytics including machine learning and Retrieval Augmented Generation.

Location: Columbia, MD; residing in the United States; hybrid work within the Eastern time zone (9:00 AM–5:00 PM ET)

Salary: $93,300–$136,400 (USD)

Company

hirify.global is a digital services company partnering with government agencies to deliver intuitive products and services for federal clients.

What you will do

• Design, develop, ingest, and maintain well-architected data pipelines from external feeds (APIs, SFTP, HTTPS/FTP, web scraping, Direct Connect) and internal agency sources into landing and curated data zones.
• Build production-grade ETL workflows using AWS Glue, PySpark, Python, Lambda, and EMR, orchestrated via Amazon Managed Workflows for Apache Airflow (MWAA).
• Load and manage data in S3 (Parquet/ORC/Iceberg), relational stores (PostgreSQL/Redshift/Oracle), NoSQL databases, and knowledge bases/vector stores while preventing duplicates and ensuring integrity and traceability.
• Implement schema enforcement, XSD validation, data quality checks, error handling, and automated notifications; generate ETL Load Reports and Gap Reports using ETL metadata.
• Develop semantic-layer objects (tables/views/materialized views) for consistent business logic and optimized query performance.
• Provide production operational support, including on-call/after-hours rotation for outages and emergencies, and support CI/CD deployments via CloudFormation.

Requirements

• 5+ years of production data engineering experience building and deploying data pipelines.
• Hands-on AWS ETL experience with Glue, Spark/PySpark, Lambda, and S3.
• Strong Python and SQL skills, including integrating data from relational databases.
• Experience processing structured and semi-structured formats (JSON, XML, CSV, Avro, Parquet) and working with lake table/file formats (Iceberg, Parquet, ORC).
• Experience with workflow orchestration (Airflow/MWAA preferred) and relational databases (PostgreSQL/Redshift/Oracle).
• Ability to obtain and maintain a U.S. Federal Public Trust security clearance (Moderate Risk); residing in the United States.

Nice to have

• Experience with financial/regulatory datasets and high-sensitivity federal data.
• CI/CD, CloudFormation/Infrastructure-as-Code, and automated testing in a federal/FedRAMP environment.
• Knowledge of Zero Trust, FISMA, NIST 800-53, Section 508, and the Privacy Act of 1974.
• Experience with knowledge bases/vector stores, Amazon Bedrock, or OpenSearch for AI/ML and RAG workloads.
• AWS certifications (e.g., AWS Certified Data Engineer).

Culture & Benefits

• Hybrid work aligned to the Eastern time zone; expected hours are 9:00 AM–5:00 PM ET.
• Comprehensive benefits package: medical, dental, vision, 401(k), paid time off and holidays, life and disability insurance, and wellness/support programs.
• Occasional travel for training and project meetings (estimated <5% per year).
• Agile team environment with sprint planning and program increment planning.
• On-call/after-hours support rotation for production outages and emergencies.

Hiring process

• Interviews to assess data engineering experience, AWS/ETL pipeline design, and production support readiness.
• Evaluation of fit for federal security clearance requirements and hybrid Eastern time zone working model.