• Lead, mentor, and coach a cross-functional team of data scientists and data engineers; monitor work assignments, track milestones, and hold staff accountable for the quality and timeliness of deliverables
• Manage stakeholder expectations by proactively communicating progress, risks, and trade-offs to technical and non-technical audiences
• Drive the end-to-end ML lifecycle, including feature engineering, model architecture and design, training, validation, deployment and monitoring
• Provide technical guidance on ML model selection, hyperparameter tuning and evaluation metrics; oversee predictive analytics solutions for project management data
• Architect scalable, resilient data pipelines using Databricks, Apache Airflow, Microsoft Fabric Data Factory and Microsoft Fabric; lead data modeling and warehousing efforts leveraging medallion architecture and Microsoft Fabric Lakehouse
• Establish and enforce engineering standards for ETL/ELT processes, code quality, version control, CI/CD, and security, including row-level and object-level controls
• Participate in and lead agile ceremonies; accurately estimate assignments and maintain technical documentation
• Evaluate emerging AI/ML frameworks and data engineering tools, making recommendations that advance team capabilities
• Assist with interviewing and onboarding new team members to ensure team sustainability

• Data Science & ML Expertise: Deep knowledge of ML model architecture and design, including supervised and unsupervised learning, deep learning, NLP, and time-series forecasting; prior experience leading data science teams and translating business problems into analytical solutions
• Data Engineering Proficiency: Expert-level understanding of ETL/ELT pipelines, data warehousing, medallion architecture and orchestration tools; prior experience leading data engineering teams building enterprise-scale data platforms
• Leadership & Accountability: Proven ability to set clear expectations, monitor deliverables, provide constructive feedback and hold team members accountable; skilled at managing stakeholder expectations across technical and business audiences
• Problem-Solving & Communication: Strong analytical skills, with the ability to break down complex problems and develop effective solutions; effective at articulating ideas and collaborating across cross-functional teams

• Programming: Python (expert), T-SQL (advanced), Spark/PySpark (advanced) 
• ML & Data Science: ML model architecture and design (advanced); model training, validation and deployment (advanced); feature engineering 
• Platforms & Tools: Databricks (advanced), Apache Airflow (advanced), Microsoft Fabric Data Factory (required); Microsoft Fabric, including Lakehouse, OneLake and semantic models (advanced) 
• Cloud & Security: Azure compute, storage, databases and developer tools (advanced); row-level and object-level security; performance monitoring and optimization 
• DevOps & Process: Azure DevOps Git, CI/CD pipelines, RESTful APIs, agile/scrum, Microsoft Power BI 

• Cloud cost optimization strategies
• Cross-tenant data sharing and Microsoft Power BI/semantic model sharing in Microsoft Fabric
• Observability tooling and platform monitoring
• Knowledge of project management and financial concepts, including budgets, revenue, profit and earned value
• Certifications in Microsoft Azure, Python, SQL or Databricks

• Bachelor’s degree in computer science, data science, statistics, mathematics or a related field; master’s degree preferred
• Seven or more years of experience in data engineering and/or data science with at least three years in a technical leadership role overseeing cross-functional data teams 

Director, Engineering

5–10 data scientists and data engineers 

Up to 5%

Exempt

This job operates in a professional office environment with standard office equipment. Remote work is supported with core hours of 9:00 a.m.–6:00 p.m. ET. The employee is regularly required to speak and listen and frequently required to stand, walk and use their hands.