Lead Data Platform Engineer

M&T Bank Corporation; Buffalo (NY); On-site; USD 116,000 - 194,000; Full time

Benefits offered by this job

Medical benefits

Retirement plan

Paid volunteer time

Job summary

M&T Bank Corporation in Buffalo, New York is seeking an experienced data engineer to design, build, and operate complex data pipelines and data products across Snowflake, Databricks and other modern platforms. You'll apply SQL, Python and CI/CD practices to deliver scalable, secure solutions.

You will mentor engineers, collaborate with product and governance teams, ensure data quality and observability, and optimize performance and costs in production environments.

Qualifications

• Bachelor’s degree or higher with relevant work experience.
• Minimum years of experience as described in the ad.
• Expert in at least one language and professional in another.
• Advanced SQL skills for complex transformations and optimization.
• Experience with Python or another language for data engineering.
• Hands-on with Snowflake and/or Databricks.

Responsibilities

• Lead design, development, testing, deployment and production support of data pipelines.
• Engineer data products and data solutions across modern platforms.
• Implement batch, streaming, CDC patterns and ensure data quality and observability.
• Apply CI/CD and infrastructure automation to delivery.
• Mentor engineers and collaborate with cross-functional teams.

Skills

Advanced SQL

Python

Education

Associate’s degree

Bachelor's degree

Master’s degree

Tools

Snowflake

Databricks

dbt

Apache Kafka

Apache Spark

Terraform

Azure Data Factory

Git-based development

CI/CD

Job description

M&T Bank Corporation in Buffalo, New York is seeking an experienced data engineer to design, build, and operate complex data pipelines and data products across Snowflake, Databricks and other modern platforms. You'll apply SQL, Python and CI/CD practices to deliver scalable, secure solutions.

You will mentor engineers, collaborate with product and governance teams, ensure data quality and observability, and optimize performance and costs in production environments.