Mid-Level Data Engineer

Chenega Corporation•Springfield, VA

About The Position

The Mid-Level Data Engineer is responsible for designing, developing, and maintaining enterprise data pipelines, dashboards, and visualization solutions that support operational monitoring, analytics, and decision-making. The role uses enterprise monitoring platforms, cloud technologies, databases, APIs, and automation tools to collect, transform, and visualize operational data while ensuring quality, reliability, and scalability. The ideal candidate has experience with Python, SQL, cloud-based data engineering, ETL/ELT processes, and data visualization, and can communicate technical findings to technical and non-technical stakeholders. The position is with Chenega Agile Real-Time Solutions (CARS), which provides integrated enterprise IT support to Federal customers in the United States and overseas. The work supports large-scale government operations and involves collaborating with stakeholders and technical teams in a fast-changing contracting environment. The position is based in Springfield, Virginia. A TS/SCI clearance and successful background and drug screenings are required.

Requirements

High school diploma or GED equivalent and at least 8 years of relevant experience, or an associate degree and at least 6 years of experience, or a bachelor's degree and at least 4 years of experience.
Experience designing and maintaining ETL/ELT pipelines.
TS/SCI clearance.
Successfully pass background and drug screening.
Strong knowledge of cloud environments such as AWS, Azure, or GCP.
Familiarity with data warehousing, data modeling, and version control systems such as Git.
Excellent analytical, organizational, and communication skills.
Proficiency in Python, SQL, and working with RESTful APIs.
Ability to work independently and effectively as part of a team.
Ability to manage multiple efforts with time-related constraints in a fast-paced contracting environment.
Ability to communicate and collaborate effectively with diverse internal and external stakeholders.
Friendly presence, helpful attitude, good interpersonal skills, and ability to work well with others.
Excellent skills in Microsoft Word, Excel, and other Office applications.
Proficiency with Microsoft Office applications and experience working in a home office setting; ability to train end users on frequently asked technical issues.
Ability to provide technical assistance and support over the phone, with good phone skills and a professional demeanor.
Good problem-solving skills, including the ability to visualize a problem or situation and think abstractly to solve it.

Nice To Haves

Experience designing and developing dashboards, reports, and data visualizations using Grafana, Power BI, Tableau, Kibana, or similar platforms.
Experience automating data workflows using Apache Airflow or similar orchestration tools.
Experience with cloud platforms and services such as AWS S3, Lambda, or Glue; Azure Data Factory; or Google BigQuery for data storage, processing, and orchestration.
Familiarity with secure, scalable, and cost-effective cloud data solutions.
Familiarity with CI/CD practices in data engineering environments.
Previous customer service experience is strongly desired.

Responsibilities

Integrate operational events, service impacts, and system metrics from enterprise monitoring tools such as Grafana, Prometheus, and ServiceNow into dashboards and visualizations.
Learn and apply new enterprise monitoring, visualization, analytics, and data platforms as business needs evolve.
Design, build, and maintain interactive dashboards, reports, charts, and visualizations that communicate operational performance and actionable insights.
Translate monitoring and analysis results into operational metrics that support decision-making.
Collect, clean, transform, and organize data from SQL and NoSQL databases, APIs, streaming services, and flat files.
Build, maintain, and optimize ETL/ELT pipelines and data ingestion workflows for centralized data repositories and reporting.
Implement data validation and quality checks to maintain data integrity.
Develop scripts and automation to streamline repetitive tasks and improve operational efficiency.
Investigate and troubleshoot data pipeline, observability, monitoring, and reporting issues independently and collaboratively.
Collaborate with stakeholders, managers, analysts, and technical teams to translate business requirements into scalable data solutions.
Maintain documentation for data systems, workflows, and business rules.
Use Git for version control and follow development best practices to support reliable deployment and maintenance of data solutions.
Present technical findings to technical and non-technical audiences.
Collaborate with team members and share knowledge of tools, processes, and best practices.
Analyze large datasets to identify trends, anomalies, and opportunities for operational improvement.
Apply data modeling and data storytelling techniques to turn raw data into actionable insights.
Perform other duties as assigned.