Note: The job is a remote job and is open to candidates in USA. isolved is hiring a Senior Data Platform Engineer to own the day-to-day operation and improvement of its data platform infrastructure and pipelines. The role administers and tunes Databricks and Snowflake, maintains ingestion and transformation workflows, and operates platform automation, security, monitoring, and incident response within architectural standards set by the data architecture team.
Responsibilities
- Administer and tune Databricks workspaces on a day-to-day basis, including Unity Catalog, access controls, service principals, cluster policies, secrets management, and cost optimization
- Administer and tune Snowflake on a day-to-day basis, including warehouses, roles and RBAC, resource monitors, and secure data sharing configuration
- Build, monitor, and maintain Fivetran connectors and incremental ingestion pipelines, troubleshooting sync failures and data quality issues as they arise
- Build, test, and maintain dbt models, tests, and documentation, keeping transformation pipelines reliable and well-governed
- Write and maintain Terraform modules to provision and manage Databricks, Snowflake, and cloud infrastructure (AWS and/or Azure) as code
- Build and maintain CI/CD pipelines, primarily in GitHub Actions, to automate testing, deployment, and infrastructure changes across the data platform
- Write PySpark and Python code to build, optimize, and maintain data processing jobs and internal automation tooling
- Monitor platform and pipeline health, troubleshoot issues, and lead or support incident response and root-cause analysis for the data platform
- Implement identity, access, and security controls (e.g., IAM, RBAC, secrets management) in line with standards set by the principal architects
- Support pipeline orchestration (e.g., Airflow or equivalent) to sequence and monitor data workflows across ingestion, transformation, and delivery
- Configure, maintain, and enhance data visualization tools like Power BI
- Contribute to architecture discussions, documentation, and standards under the guidance of the Principal Platform Architect and Principal Data Architect, without owning target-state architecture directly
- Partner with data engineering, data analytics, and the integrations team to translate architectural standards into working, well-operated systems
- Identify opportunities to improve platform reliability, automation, and cost efficiency, and raise architectural implications to the principal architects when they extend beyond day-to-day execution
- Document architectural diagrams, operational runbooks, configurations, and troubleshooting guides for the systems you own
Skills
- Typically requires a minimum of 6 years of related experience with a Bachelor's degree; 6 years and a Master's degree; or equivalent combination of education and hands-on experience in data platform engineering, infrastructure engineering, or data engineering
- Bachelor's degree in computer science, software engineering, information systems, or related field, or equivalent practical experience
- 5+ years of hands-on experience in data platform engineering, infrastructure engineering, data engineering, or a closely related discipline
- Seasoned customer engagement experience
- Deep, hands-on experience administering Databricks, including Unity Catalog, cluster policies, service principals, secrets management, and cost optimization
- Deep, hands-on experience administering Snowflake, including warehouses, roles/RBAC, resource monitors, and secure data sharing
- Deep experience building and maintaining Fivetran connectors and incremental ingestion pipelines
- Deep experience with dbt, including models, tests, documentation, and CI integration
- Deep experience building and maintaining Workato recipes and integration workflows
- Strong CI/CD experience
- With GitHub Actions, including automated testing and deployment pipelines
- Strong Terraform experience provisioning and managing cloud and data platform resources as code
- Strong PySpark experience for large-scale data processing, and solid Python skills for automation and internal tooling
- Working knowledge of Azure and/or AWS cloud infrastructure
- Comfort with Git-based version control and collaborative development workflows
- Strong troubleshooting, root-cause analysis, and incident response skills
- Ability to operate effectively within architectural standards set by others, while still contributing ideas and technical judgment to architecture discussions
- Strong communication skills and the ability to work cross-functionally with architects, data engineering, analytics, and integrations teams
- Professional services experience a huge plus
Company Overview
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