Manager, Data Engineer

Description

• Own and evolve the Azure-based data platform architecture leveraging ADLS, ADF, Microsoft Fabric, and Azure Synapse.
• Lead end-to-end platform development by defining scalable, secure, and performant data blueprints.
• Drive Infrastructure-as-Code (IaC) and automated deployments using Azure DevOps and CI/CD practices.
• Design and oversee complex data integration pipelines across ELN, LIMS, MES, and ERP systems using SnapLogic or equivalent iPaaS solutions.
• Enable downstream analytics and AI by partnering with data scientists to operationalize models using Azure Machine Learning.
• Establish and enforce data governance standards, including data quality, lineage, FAIR principles, and ALCOA+ compliance.
• Provide technical leadership, mentorship, and best-practice guidance to data engineering teams.

Qualifications

• Educational & Experience Background: Bachelor’s/Master’s degree in Computer Science, Engineering, Data Science, or related field, with 7+ years of progressive experience in data engineering, data architecture, or platform engineering.
• Technical Expertise in Cloud & Data Platforms: Proven track record designing, delivering, and operating enterprise‑scale, cloud‑native data platforms, preferably within regulated industries such as life sciences, chemicals, manufacturing, or healthcare.
• Hands-On Azure & Data Engineering Skills: Deep practical expertise with Azure ADF, ADLS, Synapse, Azure ML, Microsoft Fabric, along with advanced Python and SQL skills for building scalable data pipelines; experience with data orchestration, API design, and ingestion patterns.
• DevOps & Integration Capabilities: Hands-on experience with CI/CD pipelines, Git version control, IaC tools (ARM, Bicep, Terraform), and familiarity with iPaaS platforms like SnapLogic or equivalent technologies.
• Data Governance & Compliance Knowledge: Strong understanding of data governance, data quality, metadata, lineage, and experience implementing platforms aligned with FAIR principles and compliant with ALCOA+ and GxP standards.
• Leadership & Collaboration: Demonstrated ability to lead and mentor data engineering teams, communicate complex concepts to cross-functional stakeholders, and work effectively in global, collaborative environments with a strategic, cloud‑native mindset.