Senior Data Platform Engineer
New
J
JobgetherFitness SaaS
Based in the United StatesFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- At least 5 years of experience in data engineering, data platform engineering, data infrastructure, or software engineering, with ownership of production infrastructure from design through operation.
- Required Skills
- PythonSQLAirflowTerraform
Requirements
- At least 5 years of experience in data engineering, data platform engineering, data infrastructure, or software engineering, owning production infrastructure from design through operation.
- Strong experience with cloud data warehouses, including performance optimization, cost management, access controls, role-based access control, data masking, and schema design.
- Advanced proficiency in Python and SQL, supported by strong software engineering practices.
- Experience with automated testing, CI/CD, code reviews, and Infrastructure as Code tools such as Terraform.
- Hands-on experience operating workflow orchestration platforms such as Airflow or Dagster in production.
- Experience managing data ingestion tools such as Hevo or Fivetran and building custom pipelines when needed.
- Practical experience with change data capture, streaming, or event-driven architectures.
- Experience implementing data quality checks, monitoring, and alerting.
- Daily use of AI tools, including an AI-powered workflow used by others and a validation mechanism for inaccurate outputs.
- Knowledge of privacy and governance requirements under GDPR, CCPA, or PCI.
Responsibilities
- Build and maintain data infrastructure supporting reporting and data-driven products.
- Establish data lineage, freshness, availability, and reliability standards, and investigate incidents and root causes.
- Integrate product, payment, and third-party data to create a comprehensive view of customer and member journeys.
- Partner with software engineering teams to ensure new features and integrations produce clean, structured data.
- Establish data contracts and identify breaking changes before production.
- Manage and optimize ingestion workflows, orchestration systems, and custom data pipelines.
- Design data access controls and audit trails for employees and AI agents.
- Build data foundations, semantic layers, and governed access approaches for AI applications and reporting.
- Develop technical documentation, operational runbooks, data quality checks, and monitoring alerts.
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