Role: Data Operations Engineer
What You’ll Do
Operations Support
- Monitor and triage production data pipelines, ingestion jobs, and transformation workflows (e.g. dbt, Snowflake tasks)
- Manage and resolve data incidents and operational issues, working cross-functionally with platform, data, and analytics teams
- Develop and maintain internal tools/scripts for observability, diagnostics, and automation of data workflows
- Participate in on-call rotations to support platform uptime and SLAs
Data Platform Engineering Support
- Help manage infrastructure-as-code configurations (e.g., Terraform for Snowflake, AWS, Airflow)
- Support user onboarding, RBAC permissioning, and account provisioning across data platforms
- Assist with schema and pipeline changes, versioning, and documentation
- Assist with setting up monitoring on new pipelines in metaplane
Data & Analytics Engineering Support
- Diagnosing model failures and upstream data issues
- Collaborate with analytics teams to validate data freshness, quality, and lineage
- Coordinate and perform backfills, schema adjustments, and reprocessing when needed
- Manage operational aspects of source ingestion (e.g., REST APIs, batch jobs, database replication, kafka)
(confirm the writeup with Jason Prentice)
What We’re Looking For
Required Qualifications
- At least 2–4 years of experience in data engineering, DevOps, or data operations roles
- Solid understanding of modern data stack components (Snowflake, dbt, Airflow, Fivetran, cloud storage)
- Proficiency with SQL and comfort debugging data transformations or analytic queries
- Basic scripting/programming skills (e.g., Python, Bash) for automation and tooling
- Familiarity with version control (Git) and CI/CD pipelines for data projects
- Strong troubleshooting and communication skills — you enjoy helping others and resolving issues
- Experience with infrastructure-as-code (Terraform, CloudFormation)
- Familiarity with observability tools such as datadog
- Exposure to data governance tools and concepts (e.g., data catalogs, lineage, access control)
- Understanding of ELT best practices and schema evolution in distributed data systems
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