Job
Description
Data Operations Engineer What You’ll Do 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) (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
