Data Integration Engineer

Keep production data pipelines running across a cloud data warehouse and a real-time streaming platform, catching problems before they turn into outages Tune warehouse performance, manage access roles, and keep compute costs in check as usage grows Configure and maintain streaming infrastructure (topics, partitions, consumer groups) so data moves in near real time without gaps or duplicates Build automated checks that catch bad data, failed loads, or delivery delays before anyone downstream notices Write scripts and small tools in Python and SQL to cut down manual, repetitive pipeline work Support CI/CD for data infrastructure changes so updates ship safely and roll back cleanly if something breaks Work with data engineering and analytics teams to fix root causes, not just symptoms, when pipelines fail What we're looking for Hands‑on production experience running and tuning a cloud data warehouse (Snowflake or similar) Real, working experience with a streaming platform (Kafka or similar): producers, consumers, schema handling, and troubleshooting Comfortable in SQL and at least one scripting language, Python preferred Experience with a major cloud provider (AWS, Azure, or GCP) Familiarity with orchestration tools like Airflow or dbt Solid grasp of data modeling and the full data lifecycle, from ingestion to delivery Able to explain a pipeline issue clearly to both engineers and non‑technical stakeholders #J-18808-Ljbffr STEM Search Group