Senior Data Platform Engineer

isolved•,

About The Position

The Senior Data Platform Engineer is a hands-on technical owner of isolved's data platform infrastructure and day-to-day operations, working within the architectural vision set by the data architecture team. This role carries a lighter architectural mandate, though architectural thinking is still highly valued, in exchange for deeper, more hands-on ownership of the systems that keep the platform running: administering and tuning Databricks and Snowflake, building and maintaining Fivetran, dbt, and Workato pipelines and integrations, and operating CI/CD and infrastructure-as-code practices in Terraform and GitHub Actions. This person is the engineer who configures, monitors, troubleshoots, and continuously improves isolved's data systems on a daily basis, escalating and partnering with the architects on larger design decisions while owning execution end to end.

Requirements

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; professional services experience a huge plus.
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, ideally 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.

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.