Lead Data Engineer

3M is seeking a Lead Data Engineer to design, develop, and optimize data platforms for advanced analytics and machine learning applications. The role leads scalable data pipelines and end-to-end data engineering, collaborates with data science and ML teams, and guides engineering practices and junior engineers.

Responsibilities:

• Lead the architecture and development of scalable, secure data pipelines supporting AI/ML workloads
• Own end to end data engineering processes: ingestion, transformation, storage, quality, and monitoring
• Collaborate with data scientists and ML engineers on model features, training pipelines, and deployment
• Drive best practices in data modeling, orchestration, versioning, and performance optimization
• Mentor and guide junior engineers; contribute to technical roadmaps and solution patterns
• Ensure data governance, lineage, and compliance standards are met across platforms
• Support real time and batch processing frameworks in production environments
• Responsibilities of this position include that corporate policies, procedures and security standards are complied with while performing assigned duties
• Safety is a core value at 3M. All employees are expected to contribute to a strong Environmental Health and Safety (EHS) culture by following safety policies, identifying hazards, and engaging in continuous improvement

Requirements:

• Bachelor's degree or higher in computer science (completed and verified prior to start)
• Seven (7) years of data engineering experience, including leading technical initiatives in a private, public, government or military environment
• Must be legally authorized to work in country of employment without sponsorship for employment visa status (e.g., H1B status)
• Strong expertise in Python, SQL, and distributed data systems (e.g., Spark, Databricks, Synapse)
• Experience building AI/ML ready data pipelines, including feature stores and model training data flows
• Hands on experience with cloud platforms (Azure preferred — Data Lake, Data Factory, Databricks, Cosmos DB)
• Strong understanding of ML concepts, lifecycle, and MLOps practices
• Proven experience with workflow orchestration (Airflow, Data Factory, Synapse Pipelines, etc.)
• Strong communication and ability to translate business needs into technical solutions
• Experience with streaming platforms (Kafka, Event Hub)
• Familiarity with vector databases, embeddings, or LLM oriented data pipelines
• Background in DevOps, CI/CD, or infrastructure as code
• Experience designing data solutions for AI applications or intelligent platforms