Data Operations Engineer

Role & responsibilities

What Youll 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)

ML-Ops & Data Science Infrastructure

• Collaborate with the data science team to operationalize and support ML pipelines, removing the burden of infrastructure ownership from the team
• Monitor ML batch and streaming jobs (e.g., model scoring, feature engineering, data preprocessing)
• Maintain and improve scheduling, resource management, and observability for ML workflows (e.g., using Airflow, SageMaker, or Kubernetes-based tools)
• Help manage model artifacts, metadata, and deployment environments to ensure reproducibility and traceability
• Support the transition of ad hoc or experimental pipelines into production-grade services.

Preferred candidate profile

• At least 24 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