Platform Data Engineer

360insights orchestrates how brands engage and grow their complex partner ecosystems. Our unified platform brings together streamlined experiences, incentives, and services so brands can coordinate go-to-market efforts with clarity, consistency, and control. Built for organizations with indirect channels and complex global incentive programs, we are trusted by leading brands worldwide.

We believe in “Experiences that create growth. Growth that creates experiences.” Through learning and development programs and mentorship, we provide the support, tools, and opportunities you need to grow and succeed as an Insighter.

We welcome talent from all backgrounds and are committed to equal opportunity regardless of race, religion, ethnicity, age, disability, sexual orientation, or gender identity. Accommodations are available throughout the hiring process upon request. Learn more about our DEIB vision on our website.

The Data Platform Engineer is a hands-on, mid-level role responsible for building, maintaining, and enhancing 360insights' Microsoft Fabric data platform. Reporting to the Head of Data Platform, this position contributes to the delivery of reliable, scalable analytics solutions that support business reporting, operational insights, and client-facing reporting needs.

The role spans the full data lifecycle, including data ingestion, transformation, pipeline development, platform operations, documentation, Power BI development, and reporting modernization. Working within an AI-assisted engineering environment, the successful candidate will leverage modern development practices, automated workflows, and data platform technologies to improve reporting reliability, efficiency, and scalability.

This role is ideal for someone who enjoys combining technical problem-solving, data engineering, and business intelligence development while taking ownership of assigned data assets and contributing to platform evolution.

• Build and maintain data transformation processes within Microsoft Fabric using PySpark notebooks and Dataflow Gen2.
• Develop and support bronze, silver, and gold data models following established architecture and modeling standards.
• Implement incremental loading strategies, data quality validations, change data capture (CDC), watermarking, and Delta MERGE patterns.
• Analyze and validate transformed data against legacy reporting sources and document findings.

• Monitor and support data pipelines, lakehouses, semantic models, and platform processes.
• Investigate failures, identify root causes, and implement corrective and preventative solutions.
• Manage assigned workspace access, gateways, data connections, and related platform configurations.
• Monitor platform utilization, identify performance bottlenecks, and recommend improvements.
• Maintain platform monitoring and inventory solutions that track operational health and assets.

• Develop and maintain Power BI semantic models, dashboards, and reports.
• Create and validate DAX measures using established standards and best practices.
• Partner with stakeholders to gather reporting requirements and validate business outcomes.
• Develop and support custom internal and client-facing reports using governed Fabric data.
• Assist with the migration of legacy SSIS and TeamCity reporting workflows to Microsoft Fabric.
• Build and maintain automated report generation and distribution processes.

• Maintain accurate documentation for data structures, transformations, and business rules.
• Contribute updates through established documentation and review processes.
• Ensure schema references, wiki content, and platform documentation remain current and accurate.
• Support documentation audits and resolve gaps, inconsistencies, or outdated information.

• Participate in design discussions and recommend platform enhancements.
• Follow established AI-assisted engineering and review workflows.
• Use AI-supported development tools to improve solution quality and productivity while validating outputs independently.
• Collaborate with data, reporting, and business stakeholders to support operational and strategic objectives.

• 2-5 years of experience in Data Engineering, Analytics Engineering, Business Intelligence, or a related discipline.
• Experience working with modern cloud data platforms such as Microsoft Fabric, Databricks, Synapse, or Snowflake.
• Experience building and supporting production data pipelines and data transformation processes.
• Experience using Git-based development workflows, including branching, pull requests, and code reviews.
• Experience working with AI-assisted development tools and willingness to adopt AI-enabled workflows.

• Strong SQL skills, including:
• • Complex joins
  • Window functions
  • Query optimization and performance analysis
• Proficiency in Python and experience with PySpark or similar data transformation frameworks.
• Knowledge of data warehousing, data modeling, and ETL/ELT best practices.
• Experience implementing:
• • Incremental data loads
  • Data quality validation
  • Change Data Capture (CDC)
  • Delta Lake or similar technologies
• Power BI development experience, including:
• • Semantic modeling
  • DAX development
  • Report and dashboard creation

• Strong analytical and problem-solving abilities.
• Ability to independently investigate issues and validate solutions using evidence and data.
• Strong attention to detail and commitment to documentation.
• Ability to balance independent ownership with appropriate escalation and collaboration.
• Effective communication and stakeholder management skills.
• Continuous learning mindset and adaptability to new technologies and ways of working.

• Microsoft DP-600 or DP-700 certification.
• Experience with Delta Lake and medallion architecture.
• Experience with SQL Server, MySQL, or Amazon Redshift.
• Familiarity with Markdown-based documentation or docs-as-code practices.
• Experience supporting incentive, rebate, or loyalty program data environments.
• Experience with Power BI Paginated Reports, SSIS, TeamCity, custom report generation, or automated report distribution workflows.

• Comfortable using AI-supported development tools to improve productivity and quality.
• Able to guide AI tools effectively, review generated outputs critically, identify errors, and validate results independently.

Originally posted on Himalayas