Data Ops Engineer

Responsibilities

• Design, build, and maintain real-time data ingestion pipelines.
• Ensure data quality, observability, and scalability.
• Partner closely with data engineers, platform engineers, and analytics teams.
• Deliver trustworthy, low-latency data that supports operational decisions.
• Aid the team in delivering continual data feeds to users and monitoring the status of the health of data quality and overall data ingest.
• Employ a variety of data manipulation and visualization tools to effectively convey status and historical trends to leadership, users, and data team.
• Collaborate with platform, software, and other data engineers to (re)configure data ingestion pipelines to be more reliable.
• Support the incident management process to ensure that incidents are documented and resolved quickly.
• Perform root cause analysis to understand and prevent repeated occurrences of data outages.
• Develop and maintain software to automate monitoring of real-time feeds and alert for timeliness, volume, lineage, and distribution data issues.
• Translate historical data from data pipelines into actionable steps to improve data ingest.
• Partner with security and governance teams to enforce encryption, authentication authorization, and data classification.
• Demonstrate proficiency with frequent-used scripting languages (Python, bash) commonly used in data science applications and data analytics.

Qualifications

• U.S. citizenship and an active TS/SCI clearance.
• Bachelor of Science required in Computer Science, Mathematics, EE, Physics, Information Systems, or Information Technology.
• 3+ years of experience in data engineering, data operations, or DevOps roles supporting production data pipelines.
• Experience with NiFi, Kafka, Grafana, Prometheus, Apache Flink/Spark Streaming, Snowflake, Elasticsearch, Kafka, MQTT, JMS.
• Proficiency with Windows and Linux (RedHat).