8+ years in Data Engineering, Machine Learning Engineering, or DevOps, with 3+ years leading Databricks MLOps/Data platform implementations
Advanced proficiency in Azure Databricks/AWS Databricks, PySpark, MLflow, Delta Lake, Unity Catalog, and Python
Strong experience building automated CI/CD/CT pipelines using Databricks Asset Bundles (DABs), Azure DevOps, or GitHub Actions
Deep understanding of enterprise security controls, secret management (Key Vault/KMS), and multi-environment deployment isolation
Proven track record in client-facing technical lead role with strong communication skills for enablement and workshops
Responsibilities
Design and govern end-to-end ML production lifecycles
Automate CI/CD/CT pipelines to streamline deployments
Lead upskilling initiatives for onsite engineering teams
Bridge platform engineering with client-side operations
Ensure data governance and security controls are adhered to
Benefits
Significant career development opportunities
Entrepreneurial environment with high degree of individual responsibility
Full Job Description
We are seeking an experienced MLOps Lead Engineer to take full ownership of our enterprise Machine Learning operations on the Databricks Lakehouse platform. In this role, you will design, automate, and govern end-to-end ML production lifecycles-spanning continuous integration and deployment (CI/CD/CT), central governance, and real-time observability. You will act as the principal technical authority, bridging platform engineering with client-side operations by leading upskilling initiatives for onsite engineering teams.
Requirements Required Qualifications & Experience
Experience: 8+ years in Data Engineering, Machine Learning Engineering, or DevOps, with 3+ years specifically leading Databricks MLOps/Data platform implementations.
Core Technical Stack: Advanced proficiency in Azure Databricks / AWS Databricks, PySpark, MLflow, Delta Lake, Unity Catalog, and Python.
Automation & DevOps: Strong experience building automated CI/CD/CT pipelines using Databricks Asset Bundles (DABs), Azure DevOps, or GitHub Actions.
Governance & Security: Deep understanding of enterprise security controls, secret management (Key Vault/KMS), and multi-environment deployment isolation.
Stakeholder Management: Proven track record in a client-facing technical lead role, with strong communication skills to drive enablement, workshops, and technical handovers.
Benefits
This position offers an excellent opportunity for significant career development in a fast-growing and challenging entrepreneurial environment with a high degree of individual responsibility.
About Tiger Analytics
Tiger Analytics is a consulting firm that provides data analytics consulting services to businesses. The company specializes in data science, machine learning, and artificial intelligence. Tiger Analytics helps businesses to leverage data to make better decisions, improve operations, and drive growth. The company has worked with clients in a variety of industries, including healthcare, retail, finance, and technology.
