Manager, Data Engineering-ML Ops

This position is responsible for developing and maintaining the necessary infrastructure for deploying, monitoring, and optimizing machine learning models. It plays a pivotal role in strategizing, designing, and implementing robust data pipelines, ensuring the scalability and reliability of our ML systems, and collaborating with cross-functional teams to advance our data-driven initiatives.

You will also oversee a team of data engineers and be responsible for building a data centric culture within the team.

What will you do?

 Data Pipeline/ ML Model Deployment

• Design, build, and maintain that support the ingestion, transformation, and storage of large-scale datasets.
• Optimize data workflows to ensure efficient data processing and availability.
• Develop and implement best practices for deploying, monitoring, and managing robust data pipelines and ML models in production.
• Collaborate with data scientists to ensure seamless integration and deployment of ML solutions.

Strategy and Planning

• Define the vision, strategy, and roadmap for data engineering and ML Ops in alignment with the company's goals.
• Ensure the scalability, reliability, and efficiency of ML models and data pipelines.

Team Leadership and Development

• Lead and motivate a high-functioning engineering team, providing coaching to empower members and delivery squads.
• Encourage innovation, experimentation, and best practices within the data engineering team.
• Allocate resources, manage workloads, and set goals to ensure timely, budget-compliant project completion.

Security and Compliance

• Ensure that data engineering and ML Ops processes adhere to industry standards, regulatory requirements, and security best practices.
• Implement measures to safeguard sensitive data and protect against potential vulnerabilities

Continuous Improvement

• Stay updated on the latest advancements in data engineering and ML Ops technologies and methodologies.
• Implement best practices and drive continuous improvement initiatives to enhance the efficiency and effectiveness of our ML systems.

What will you need to succeed in this role?

• Bachelor’s or master’s degree in computer science, Data Engineering, Software Engineering or a related field.
• Demonstrated experience (5+ years) in data engineering, machine learning operations, or related fields, including a minimum of 2 years in a leadership or managerial role.
• Advanced proficiency in programming languages such as Python, Java, or Scala. Comprehensive knowledge of data pipeline frameworks (e.g., Databricks) and ML Ops tools (e.g., MLflow, Kubeflow).
• Extensive hands-on experience with cloud platforms (e.g., AWS, Google Cloud, Azure) and containerization/orchestration tools (e.g., Docker, Kubernetes).
• Exceptional problem-solving and analytical skills, with in-depth expertise in data architecture, data modeling, and database systems.
• Strong leadership and team management capabilities, with the ability to motivate and inspire team members. Excellent communication and interpersonal proficiency.
• Ability to excel in a fast-paced and dynamic environment. Proven adaptability and eagerness to adopt new technologies and methodologies.
• High attention to detail and a commitment to delivering high-quality solutions. Ability to prioritize tasks and manage multiple projects concurrently.

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