Sr. Data Engineer, AI-Augmented

Sr. Data Engineer, AI-Augmented
West Des Moines, Iowa
Information Technology
No
5/12/2026
What You Can Expect In This Role
Enterprise data engineers in this role support the real-time data movement and shared data services layer of the Enterprise Data & Integration platform. This team is responsible for the day-to-day operational health of Change Data Capture feeds, business event streams, Kafka topics, Snowpipe ingestion, and shared data services that enterprise data products depend on.
This is a hands-on operational engineering role focused on production support, incident response, root-cause analysis, reliability, and continuous improvement. The ideal candidate is calm under pressure, methodical in troubleshooting, comfortable working across logs, SQL, Kafka, Snowflake, and Azure, and able to drive complex data pipeline incidents to resolution.
What You'll Do In This Role
Monitor the health of CDC pipelines and shared data services in production, including consumer lag, throughput, error rates, dead-letter queues, Snowpipe backlogs, and end-to-end latency.
Triage and resolve data pipeline incidents by identifying whether the issue is related to the source system, data movement layer, Kafka, Snowpipe, Snowflake, or downstream consumers, and engage the appropriate owners.
Serve in the on-call rotation for SEV-1 and SEV-2 data incidents in alignment with the Data Pipeline Incident Response runbook.
Diagnose schema-evolution issues in Confluent Schema Registry, including incompatible changes, deserialization failures, and consumer poison messages, and coordinate fixes with publishing teams.
Replay, reprocess, and backfill data through Kafka and Snowpipe when upstream systems recover from outages, resend data, or provide corrections.
Investigate data-quality issues reported by consumers, including missing records, duplicates, late-arriving data, out-of-order events, and mismatched counts between source systems and hist_• / curr_• zones in Snowflake.
Tune and right-size pipeline components for performance, reliability, and cost efficiency, including Kafka partitions and consumers, Snowpipe warehouses, and Azure Function scale settings.
Maintain and improve operational tooling, including monitoring dashboards, alerting, and replay utilities across Confluent, Snowflake, Azure Monitor, and Application Insights.
Write, maintain, and improve operational runbooks so recurring issues can be resolved faster and more effectively by first-level support teams.