Skip to content
Senior Data Science Workbench Analyst – Engineer – Databricks or Snowflake
| Company | M&T Bank |
|---|
| Location | Bridgeport, CT, USA |
|---|
| Salary | $119400.84 – $199001.4 |
|---|
| Type | Full-Time |
|---|
| Degrees | Bachelor’s, Master’s |
|---|
| Experience Level | Senior |
|---|
Requirements
- Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, or related field.
- 5+ years of experience in cloud-based infrastructure management and AI/ML workbench administration.
- Expertise in Databricks, and cloud-based data platforms (AWS, Azure, GCP).
- Strong programming skills in Python, SQL, and automation scripting (Terraform, Bash, or similar).
- Experience with workflow orchestration tools such as Apache Airflow or Prefect.
- Deep understanding of cloud security, IAM roles, and governance best practices.
- Proven ability to lead projects and mentor junior engineers.
Responsibilities
- Lead automation initiatives to improve the scalability and efficiency of AI/ML workflows.
- Architect and maintain highly available cloud-based data science environments on platforms like Databricks and Snowflake.
- Enhance monitoring and observability for AI/ML infrastructure, ensuring performance optimization and cost efficiency.
- Improve and enforce security and compliance measures across data science environments.
- Develop and refine CI/CD pipelines to streamline model deployment and management in production.
- Collaborate with data scientists and engineers to drive innovation and operational excellence.
- Mentor junior engineers by providing guidance on infrastructure best practices, cloud security, and automation.
- Optimize cloud cost management strategies to ensure efficient resource utilization.
Preferred Qualifications
- Certifications: AWS Solutions Architect Professional, Databricks Advanced Developer, Snowflake Advanced Architect.
- Experience integrating machine learning models into production pipelines.
- Proficiency in Kubernetes, Docker, and containerized AI/ML workloads.
- Experience working with real-time data streaming technologies such as Kafka or Kinesis.
- Strong knowledge of FinOps for cloud cost optimization.
