Mid-Level Data Engineer

Benefits:

• 401(k)
• Dental insurance
• Flexible schedule
• Health insurance
• Paid time off
• Parental leave

Mid-Level Data Engineer Position Overview Meridian Systematics is seeking a Data Engineer to support a National Geospatial-Intelligence Agency (NGA) program that delivers GEOINT data across the enterprise. You will design and build the pipelines and integrations that change how analysts, customers, and end users access, discover, stage, link, produce, and deliver intelligence. You will partner with senior engineers and mission teams to deliver reliable data across cloud and enterprise environments.

Responsibilities

• Design, build, test, and maintain data pipelines and ETL/ELT workflows for analytics, reporting, and mission operations.
• Develop and maintain API connectors and integrations that ingest data from internal platforms and external services.
• Create and improve data models and processing workflows to meet performance, scale, and reliability needs.
• Implement data validation, quality checks, monitoring, and documentation for assigned data products.
• Work with mission stakeholders, analysts, and architects to clarify requirements and deliver maintainable solutions.
• Troubleshoot pipeline and integration issues, support production deployments, and raise risks or complex issues to the team.

Qualifications

Required:

• Active TS/SCI clearance with CI Polygraph.
• Bachelor's degree with 3–5 years of relevant experience, or a related master's degree with 1–3 years. Degree in computer science, information systems, engineering, mathematics, or a related field; equivalent education and experience may be considered.
• Experience writing readable, tested, and documented code in Python, R, Scala, JavaScript, or C++, using Git.
• Practical experience with ETL/ELT workflows, relational data, and common structured and semi-structured data formats.
• Experience building, testing, and supporting data pipelines or integrations in a team environment.
• Experience automating data acquisition using visual programming or object-oriented modeling workflows.
• Ability to apply data validation and analytical techniques to investigate data quality issues and assess results.
• Ability to choose appropriate transformation approaches, explain tradeoffs, and recognize when an issue needs senior review.
• Working knowledge of data modeling, metadata, or data catalog concepts.
• Exposure to cloud data platforms or tools such as AWS, Spark, Hadoop, Hive, Elasticsearch, or Tableau.

Preferred:

• Experience with geospatial or imagery data.
• AI/ML experience.
• Knowledge of metadata standards.
• Experience with search tools, search results organization, indexing, and how metadata organization affects catalog content.
• Experience with GIS-related applications.