Senior Data Engineer — Spark & Airflow Pipelines (TS/SCI)

CTRL + LLC; Fort Meade (MD); On-site; USD 170,000 - 188,000; Full time

Benefits offered by this job

10% employer 401(k) contribution

Life insurance

Disability insurance

Medical, dental and vision coverage

Employee Assistance Program

Certification reimbursement

Equipment reimbursement

Referral and retention bonuses

Paid social and team events

Job summary

Ctrl+ is seeking a data engineer to design data pathways and build reliable ingest and egress flows powered by Spark and Airflow. You will support distributed data processing and analytics workloads, working on large, varied datasets at Fort Meade, MD.

The role involves designing data ingress/egress, tuning Spark jobs, and orchestrating Airflow DAGs while ensuring data quality and validation. In-person attendance is required, with competitive hourly compensation and multiple pay models.

Qualifications

• Seven years of relevant experience with a Bachelor’s in Computer Science or related discipline (5 years with a Master’s, or 9 years without a degree).
• Linux command-line proficiency and Bash scripting.
• Recent Python and Java development experience.

Responsibilities

• Design and optimize data ingress and egress pathways
• Build and tune Apache Spark distributed processing jobs
• Orchestrate and monitor workflows with Apache Airflow
• Wrangle structured, semi-structured and unstructured data
• Apply data quality, validation and anomaly detection practices

Skills

Data engineering

Spark

Airflow

Python

Java

SQL

Linux

Bash scripting

Git

Jupyter

Education

Bachelor’s degree in CS

Tools

Jira

Confluence

Job description

Ctrl+ is seeking a data engineer to design data pathways and build reliable ingest and egress flows powered by Spark and Airflow. You will support distributed data processing and analytics workloads, working on large, varied datasets at Fort Meade, MD.

The role involves designing data ingress/egress, tuning Spark jobs, and orchestrating Airflow DAGs while ensuring data quality and validation. In-person attendance is required, with competitive hourly compensation and multiple pay models.