Data Ops Engineer

SAIC•San Diego, CA

1d•$200,001 - $240,000•Onsite

About The Position

We are seeking a Data Ops Engineer to design, build, and maintain real-time data ingestion pipelines. In this role, you will be responsible for the reliable flow of streaming data from a wide range of sources into our data platform, ensuring data quality, observability, and scalability. You'll partner closely with data engineers, platform engineers, and analytics teams to deliver trustworthy, low-latency data that supports operational decisions. This position is on-site in San Diego, CA.

Requirements

U.S. citizenship and an active TS/SCI
Bachelor of Science required in the following preferred fields: 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.
Proficiency with frequent-used scripting language (Python, bash) commonly used in data science applications and data analytics.
Experience with operating systems: Windows, Linux (RedHat).
Experience with tools/apps/platforms: NiFi, Kafka, Grafana, Prometheus, Apache Flink/Spark Streaming, Snowflake, Elasticsearch, Kafka, MQTT, JMS.

Responsibilities

Aid the team in delivering continual data feeds to users and monitoring the status of the health of data quality and overall data ingest.
Build resilient pipelines with appropriate backpressure, prioritization, retries, and error-handling strategies.
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.
Work with data in a variety of formats including Excel, CSV, JSON, and XML.
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.
Process learnings and rely on historical data from data pipelines, translating them into actionable steps to improve data ingest.
Partner with security and governance teams to enforce encryption, authentication authorization, and data classification.