• Design, build, and maintain scalable data pipelines (ETL/ELT) and data models • Extract, transform, and load data from ERP, CRM, field service systems, and blob or database storage • Provide clean, well-structured, and documented datasets, including analytics-ready dbt models • Partner with analytics and business teams to ensure data is accessible, understandable, and fit for reporting and analysis • Assist with data integration for acquisitions • Support the data foundation required to track value creation initiatives and operational improvements • Implement data quality rules and operate within data governed environments.

• Define, evolve and ensure adoption of data architectures on Azure, aligned with business strategy and industry best practices • Act as a technical reference on complex initiatives, supporting architectural and technology decisions • Lead implementation of large-scale data solutions using services such as Azure Data Factory, Azure Databricks and Azure Synapse • Establish standards, frameworks and data engineering best practices, ensuring quality, governance and security • Design, build and optimize data pipelines with a focus on performance, scalability and operational efficiency • Drive modernization and migration initiatives for data platforms to the cloud • Collaborate with business, architecture, security and engineering teams to ensure integrated and sustainable solutions • Serve as a technical mentor, supporting squads and sharing knowledge across the team • Analyze and optimize costs, performance and cloud resource utilization, driving continuous improvements

• Design, build, and maintain scalable, fault-tolerant data pipelines (batch and/or streaming) for core business and product data. • Ingest data from diverse sources including APIs, databases, event streams, and third-party services, ensuring high data quality and reliability. • Design and manage data models and storage layers (data warehouses, data lakes) that support analytics and downstream use cases. • Partner with analytics, product, and engineering teams to deliver clean, well-documented datasets that enable self-service analytics and experimentation. • Implement monitoring, logging, and alerting to ensure pipeline reliability, performance, and cost efficiency. • Enforce data governance best practices, including access control, privacy, documentation, and data lineage.

• Design and build batch and streaming data pipelines to support automated data ingestion, ML feature engineering and analytics across multiple product domains. • Own end-to-end delivery of complex, ambiguous data initiatives, including architecture, implementation, testing, deployment, monitoring, and documentation. • Develop and evolve the data platform to support large-scale data processing using modern cloud-native technologies. • Automate data operations (validation, quality checks, alerting, backfills, and recovery workflows) to reduce manual effort and improve consistency. • Optimize cost, performance, and reliability of data workloads. • Partner closely with cross-functional teams (Data Science, Product, Engineering) to understand requirements, translate them into technical solutions. • Evaluate and adopt new technologies (new processing engines, storage formats, orchestration tools, GenAI-assisted ingestion) to keep the platform modern and efficient.