Job
Description
Data Operations Engineer About the Role Responsibilities Operations Support Monitor and triage production data pipelines, ingestion jobs, and transformation workflows (e.g. dbt, Fivetran, 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) 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 Qualifications Required Qualifications At least 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 Show more Show less
