Real-Time Data Ops Engineer: Pipelines & Observability

Saic; San Diego (CA); On-site; USD 200,000 - 240,000; Full time

Job summary

SAIC is seeking a Data Ops Engineer in San Diego, CA to design, build, and maintain real-time data ingestion pipelines. You will ensure low-latency, high-quality data flows across sources into our data platform, partnering with data engineers and analytics teams to support operational decisions.

The role emphasizes observability, data quality, and scalable pipelines, with responsibilities spanning backpressure strategies, data formats like CSV/JSON/XML, and automation of monitoring and incident

Qualifications

• 3+ years of experience in data engineering, data operations, or DevOps roles supporting production data pipelines.
• U.S. citizenship and an active TS/SCI clearance.
• Bachelor of Science in Computer Science or related field is preferred.

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 repeat outages.
• Develop and maintain software to automate monitoring of real-time feeds and alert for timeliness, volume, lineage, and distribution data issues.
• Partner with security and governance teams to enforce encryption, authentication, authorization, and data classification.
• Demonstrate proficiency with frequent-used scripting language (Python, bash) commonly used in data science applications and data analytics.

Skills

Data pipelines

Python

Shell scripting

Data quality

Observability

DevOps practices

Education

Bachelor of Science in Computer Science

Tools

NiFi

Kafka

Grafana

Prometheus

Flink

Spark Streaming

Snowflake

Elasticsearch

MQTT

JMS

Job description

SAIC is seeking a Data Ops Engineer in San Diego, CA to design, build, and maintain real-time data ingestion pipelines. You will ensure low-latency, high-quality data flows across sources into our data platform, partnering with data engineers and analytics teams to support operational decisions.

The role emphasizes observability, data quality, and scalable pipelines, with responsibilities spanning backpressure strategies, data formats like CSV/JSON/XML, and automation of monitoring and incident