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)
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
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