Data Engineer; Entry Level

Position Summary

Join our team as a Data Engineer and become a key contributor to building trusted, high‑quality data products for Federal clients. You will help design, implement, and operate scalable data pipelines and lakehouse solutions across major cloud platforms, including Microsoft Azure (e.g., Synapse, ADLS Gen2) and AWS (e.g., S3, Glue, Lambda). You will collaborate closely with senior engineers, product owners, analysts, and stakeholders in a Scrum environment.

The ideal candidate is a recent graduate with exceptional aptitude, a strong academic record, and the curiosity, drive, and self‑direction to grow quickly into a high‑performing data engineer.

• Help design and implement secure, scalable data pipelines using cloud‑native services such as Azure Synapse, Azure Functions, and Logic Apps, or AWS Glue, Lambda, and Step Functions, applying Python, Pandas, PySpark, and SQL to ingest, profile, transform, improve data quality, and publish trusted data.
• Contribute to robust lake and lakehouse storage on ADLS Gen2 or Amazon S3 using Parquet, Iceberg, partitioning techniques, and performance optimization techniques.
• Apply data quality controls: profiling, validation rules, reproducible transformations, and automated tests with monitoring and alerting.
• Operationalize pipelines with CI/CD, environment promotion, secrets management, and run‑time observability using tools such as Azure Dev Ops, Git Hub Actions, or AWS Code Pipeline.
• Model and serve data for analytics and BI (e.g., Power BI, Amazon Quick Sight), including row‑level security and refresh orchestration.
• Document design decisions, lineage, and data contracts; contribute to catalog and metadata management using tools such as Microsoft Purview or AWS Glue Data Catalog.
• Collaborate within a Scrum team by participating in sprint planning, reviews, and retros, and by partnering with product owners and stakeholders to refine user stories.
• Continuously improve team ways of working by proposing automation, standardization, and quality practices that increase throughput and reliability.
• Produce high quality products on schedule that meet and exceed client expectations.

• U.S. citizen with the ability to obtain a Public Trust background investigation.
• Bachelor’s degree in Data Engineering, Computer Science, or a closely related technical field (required).
• Strong academic record demonstrating mastery of core technical coursework.
• Substantial project or internship experience in data engineering, software engineering, or a closely related technical area, with concrete examples of work delivered.
• Exceptional aptitude and readiness to engage with real data engineering work from day one, including the ability to learn quickly, take ownership, and self‑direct.
• Strong understanding of programming languages and frameworks commonly used in data engineering and data science
  , including Python (Pandas, PySpark) and SQL.
• Working knowledge of source control tools
  , particularly Git, and familiarity with collaborative development workflows.
• Self‑starter mindset with a track record of initiative, follow‑through, and producing results without close supervision.
• Excellent written and verbal communication skills
  , with the ability to explain technical ideas clearly to both technical and non‑technical audiences.
• Collaborative, help‑seeking and help‑giving approach suited to a Scrum team.

• Coursework, projects, or internship exposure to cloud data platforms, such as Microsoft Azure (Synapse, ADLS Gen2, Azure Functions, Logic Apps) or AWS (S3, Glue, Lambda, Athena, Redshift).
• Experience with reporting and BI tools such as Power BI, Tableau, or Amazon Quick Sight.
• Experience with web programming frameworks such as React or Angular.
• Familiarity with data quality, lineage, and governance concepts, including tools such as Microsoft Purview or AWS Glue Data Catalog.
• Familiarity with CI/CD practices (e.g., Azure Dev Ops, Git Hub Actions, AWS Code Pipeline) for software.
• Demonstrated experience using and appropriately applying AI tools (e.g., LLM‑based assistants, AI coding tools, AI agent workflows) to accelerate…