Data Ops Engineer-Remote Python,Snowflake,ETL, DBT, AWSTerraform

Job description

Role: Data Operations Engineer 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) 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