Senior Data Engineer

The Senior Data Engineer designs builds and supports the data pipelines integrations and platform capabilities that power Malibu Boats Inc.s business applications manufacturing operations dealer ecosystem analytics and enterprise reporting.

This is a hands-on senior engineering role that bridges MBIs current SQL-based environment with its modern Microsoft Fabric data platform. The Senior Data Engineer will maintain the reliability of business-critical production integrations while progressively modernizing legacy ETL stored procedures linked-server processes and middleware workflows.

The ideal candidate combines strong SQL and production-support experience with modern cloud data engineering skills including Microsoft Fabric lakehouse architecture Python PySpark Delta Lake APIs and automated deployment practices. Success requires technical depth practical judgment end-to-end ownership and the ability to collaborate effectively across a fast-moving organization.

Essential Duties and Responsibilities

Modern Data Platform Engineering

• Design develop test deploy and operate scalable ETL/ELT pipelines within Microsoft Fabric or a comparable cloud data platform.
• Build and maintain Fabric lakehouses warehouses Data Factory pipelines notebooks SQL analytics endpoints and related platform components.
• Develop PySpark and Delta Lake solutions supporting full loads incremental processing merge/upsert patterns partitioning and schema evolution.
• Apply medallion architecture principles preserving source fidelity in Bronze creating validated and conformed data in Silver and delivering business-ready datasets through Gold.
• Build pipelines using reusable version-controlled Python components rather than embedding complex business logic entirely within notebooks.
• Implement watermark-based incremental loading write-back-on-success controls checkpointing and idempotent processing so pipelines can be safely restarted or rerun.
• Design data models and transformation patterns that balance source-system fidelity enterprise consistency performance and business usability.

Enterprise and Operational Integration

• Build and support bidirectional integrations between the enterprise data platform and operational systems including ERP CPQ CRM dealer portals internal applications vendor platforms and third-party SaaS solutions.
• Develop integrations using REST APIs webhooks SFTP JSON flat files scheduled exports middleware and database-based interfaces.
• Support operational write-back scenarios such as ERP transactions CRM updates dealer-system exchanges and downstream application feeds.
• Design integrations with appropriate transactional boundaries correlation identifiers retry logic reconciliation auditability and delivery confirmation.
• Account for the different performance latency validation and recovery requirements of analytical pipelines and operational integrations.
• Implement secure connectivity using service principals managed identities Azure Key Vault on-premises data gateways and other approved security patterns.

Current-State Production Support and Modernization

• Develop optimize and troubleshoot complex SQL queries stored procedures views database objects SQL Agent jobs and production ETL processes.
• Maintain and safely modify existing data solutions including unfamiliar or insufficiently documented code.
• Support linked servers and cross-system queries while identifying their performance security and reliability limitations.
• Operate and troubleshoot existing middleware and iPaaS workflows such as Workato including error resolution record reprocessing and changes required by source or target systems.
• Support batch-processing solutions and file-based integrations using SFTP CSV Excel and other standard enterprise formats.
• Plan data extraction around production OLTP workloads considering locking resource utilization operational schedules and system performance.
• Apply a modernization mindset to legacy support: stabilize the process document its business purpose and dependencies and prepare it for migration rather than unnecessarily extending technical debt.

Reliability Quality and Operational Excellence

• Build data solutions with validation gates zero-row protections schema-drift detection error handling structured logging monitoring and actionable alerting.
• Design pipelines to fail visibly and safely instead of silently producing incomplete duplicated or inaccurate data.
• Investigate complex data and integration incidents perform root-cause analysis and implement sustainable corrective and preventive solutions.
• Improve the performance resiliency observability scalability and maintainability of existing data processes.
• Protect data quality and completeness by reconciling delivered records preserving unresolved records when appropriate and preventing silent data loss.
• Support critical production issues and participate in scheduled after-hours support when necessary.

Engineering Practices and Collaboration

• Use Git-based engineering practices including feature branches pull requests peer reviews automated testing and controlled promotion across development test and production environments.
• Contribute to CI/CD pipelines and repeatable deployment processes for database integration and Microsoft Fabric solutions.
• Apply professional Python development practices including modular design dependency management unit testing linting and pre-commit quality checks.
• Create and maintain clear technical documentation covering data flows source-to-target mappings rename rules watermark logic architecture dependencies operational procedures and known source-system behaviors.
• Partner closely with Application Development Database Administration Infrastructure Security Analytics business teams and external vendors to deliver complete solutions.
• Participate in architecture discussions technical design reviews code reviews and the continued development of MBIs data engineering standards.
• Provide technical guidance share knowledge and help strengthen engineering practices across the Data Services team.

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Preferred Qualifications

• Hands-on experience with Microsoft Fabric including Data Factory pipelines lakehouses warehouses notebooks OneLake SQL analytics endpoints or Materialized Lake Views.
• Strong experience with Python PySpark Delta Lake and scalable incremental-processing patterns.
• Experience with Azure Data Factory Azure Functions Logic Apps Workato or another middleware/iPaaS platform.
• Experience implementing secure cloud-to-on-premises connectivity using gateways service principals managed identities or Azure Key Vault.
• Experience supporting ERP CPQ CRM manufacturing dealer supply-chain or order-to-cash systems.
• Familiarity with Python testing and quality tools such as pytest Ruff pre-commit uv or Poetry.
• Experience modernizing legacy SQL SSIS linked-server or middleware-based integrations.
• Experience operating data solutions in environments with formal security privacy governance or audit requirements.

Success in This Role

The Senior Data Engineer is expected to operate with a high degree of technical independence while remaining collaborative practical and responsive to the needs of the business. This individual will take ownership beyond writing codeasking questions early understanding the business process behind the data identifying risks documenting decisions and ensuring solutions work reliably in production.

MBI operates with a hands-on team-oriented culture. The successful candidate will be comfortable working across technical and business boundaries adapting as priorities evolve and balancing immediate operational needs with the long-term modernization of MBIs data platform.