The Role
Designs, develops, deploys, and supports complex data pipelines and data products across cloud, warehouse, and lakehouse platforms. Builds batch, streaming, CDC, and transformation solutions using SQL, Python, Snowflake, Databricks, Spark, dbt, and related technologies. Applies data modeling, quality, monitoring, security, performance, and cost-optimization practices. Participates in technical design, code reviews, Agile delivery, production support, incident resolution, and continuous improvement while mentoring less-experienced engineers without direct people-management responsibility.
Summary Generated by Built In
Job Description Overview:
Primary Responsibilities:
Education and Experience Required:
Education and Experience Preferred:
- Designs, develops, tests, deploys and supports complex data pipelines, data products and data management solutions across modern cloud, warehouse and lakehouse platforms. Applies software-engineering, data-modeling and distributed-processing practices to deliver secure, reliable, maintainable and cost-conscious solutions. Works independently on defined outcomes, collaborates across technology and data teams, and contributes to reusable engineering patterns and continuous improvement.
Primary Responsibilities:
- Design, develop, test, deploy and support complex production data pipelines and data products with guidance on broader architecture where needed.
- Develop maintainable data-processing and transformation solutions using SQL and programming languages such as Python.
- Implement solutions using Snowflake, Databricks, dbt and comparable modern data platforms and frameworks.
- Build data solutions across relational databases, cloud object storage, warehouse and lakehouse architectures, including open table formats such as Apache Iceberg where applicable.
- Develop batch, micro-batch, CDC and streaming pipelines using approved enterprise patterns and technologies such as Apache Kafka or comparable platforms.
- Integrate data from relational and mainframe systems, APIs, event streams, SaaS platforms, files and object storage.
- Apply normalized, dimensional and denormalized data-modeling techniques appropriate to analytical and data-product requirements.
- Tune SQL, Snowflake and Databricks/Spark workloads for performance and cost using appropriate partitioning, parallelism and compute practices.
- Implement data quality, reconciliation, metadata, lineage, logging, monitoring and alerting controls.
- Build pipelines that support error handling, restartability, idempotency, retry, replay and recovery using established patterns.
- Participate in production support, incident resolution and root-cause analysis; implement corrective actions to prevent recurrence.
- Use Git, peer review, automated testing, CI/CD and environment promotion practices for data engineering delivery.
- Develop and reuse shared components, templates and patterns; contribute improvements to engineering standards and documentation.
- Participate in technical design and code reviews, identify implementation risks and escalate architectural concerns appropriately.
- Partner with product owners, architects, governance, cybersecurity, platform and consuming teams to clarify requirements and deliver supported solutions.
- Contribute to technical epics and stories, estimate assigned work, identify dependencies and deliver through Agile practices.
- Provide day-to-day technical assistance and informal mentoring to less-experienced engineers. This role does not have direct people-management responsibility.
- Understand and adhere to the Company’s risk and regulatory standards, policies and controls in accordance with the Company’s Risk Appetite.
- Design, implement, maintain and enhance internal controls to mitigate risk on an ongoing basis. Identify risk-related issues requiring escalation to management.
- Maintain internal control standards, including timely implementation of internal and external audit points and issues raised by external regulators, as applicable.
- Promote an environment that supports belonging and reflects the Company brand.
- Complete other related duties as assigned.
Education and Experience Required:
- Associate’s degree and a minimum of 5 years’ systems analysis and/ or application development work experience or Bachelor's degree and a minimum of 3 years’ systems analysis and/ or application development work experience. In lieu of degree, a combined minimum of 7 years’ education and/or relevant work experience, including a minimum of 3 years’ systems analysis and/or application development work experience
- Strong SQL skills, including development of complex transformations and analytical queries.
- Experience with Python or another general-purpose programming language used for data engineering.
- Hands-on experience with Snowflake, Databricks or a comparable cloud data platform.
- Experience with ELT/ETL and transformation practices, including dbt or a comparable framework.
- Experience with relational data stores and warehouse or lakehouse data-modeling concepts.
- Experience with distributed processing using Apache Spark/PySpark or a comparable framework.
- Experience with Git-based source control, automated testing and CI/CD fundamentals.
- Experience with batch data processing and working knowledge of streaming or event-driven concepts.
- Knowledge of cloud and object-storage concepts, preferably in Microsoft Azure.
- Experience working with structured and semi-structured data using formats such as Parquet, JSON, CSV, Avro and XML.
- Experience with Linux/Unix environments and scripting.
- Demonstrated ability to independently deliver complex data pipeline components and data products within an established architecture.
- Demonstrated ability to troubleshoot data-processing, data-quality and production issues across multiple technologies.
- Demonstrated ability to apply established engineering patterns while identifying opportunities for improvement.
- Demonstrated ability to communicate implementation choices, risks and dependencies to technical and product partners.
- Demonstrated ability to manage assigned work from design through production support with limited day-to-day supervision.
Education and Experience Preferred:
- Production experience with Snowflake, Databricks, Apache Spark/PySpark and dbt.
- Experience with Apache Iceberg or another open table format.
- Experience with Apache Kafka, CDC or event-driven data processing.
- Experience with orchestration technologies such as Apache Airflow, Azure Data Factory, Databricks Workflows or dbt orchestration.
- Experience with Terraform or another Infrastructure as Code technology.
- Experience with data catalogs, metadata, lineage, observability or automated data-quality tooling.
- Experience delivering governed enterprise data products.
- Experience working in a regulated environment such as financial services.
Skills Required
- Associate's degree and at least 5 years of systems analysis and/or application development experience
- Bachelor's degree and at least 3 years of systems analysis and/or application development experience
- In lieu of a degree, at least 7 years of combined education and relevant work experience, including 3 years of systems analysis and/or application development experience
- Strong SQL skills, including complex transformations and analytical queries
- Experience with Python or another general-purpose programming language used for data engineering
- Hands-on experience with Snowflake, Databricks, or a comparable cloud data platform
- Experience with ELT/ETL and transformation practices, including dbt or a comparable framework
- Experience with relational data stores and warehouse or lakehouse data modeling
- Experience with distributed processing using Apache Spark, PySpark, or a comparable framework
- Experience with Git-based source control, automated testing, and CI/CD fundamentals
- Experience with batch data processing and working knowledge of streaming or event-driven concepts
- Knowledge of cloud and object-storage concepts, preferably Microsoft Azure
- Experience working with structured and semi-structured data formats including Parquet, JSON, CSV, Avro, and XML
- Experience with Linux/Unix environments and scripting
- Ability to independently deliver complex data pipeline components and data products within an established architecture
- Ability to troubleshoot data-processing, data-quality, and production issues across multiple technologies
- Ability to apply established engineering patterns and identify improvement opportunities
- Ability to communicate implementation choices, risks, and dependencies to technical and product partners
- Ability to manage assigned work from design through production support with limited supervision
- Production experience with Snowflake, Databricks, Apache Spark/PySpark, and dbt
- Experience with Apache Iceberg or another open table format
- Experience with Apache Kafka, CDC, or event-driven data processing
- Experience with orchestration technologies such as Apache Airflow, Azure Data Factory, Databricks Workflows, or dbt orchestration
- Experience with Terraform or another Infrastructure as Code technology
- Experience with data catalogs, metadata, lineage, observability, or automated data-quality tooling
- Experience delivering governed enterprise data products
- Experience working in a regulated environment such as financial services
M&T Bank Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about M&T Bank and has not been reviewed or approved by M&T Bank.
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Retirement Support — Retirement benefits are positioned as a strong pillar, including a 401(k) match and the possibility of an additional employer contribution, plus access to an employee stock purchase plan.
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Leave & Time Off Breadth — Time-off offerings are framed as competitive, with a flexible PTO approach and paid volunteer time called out as a meaningful add-on to standard leave.
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Wellbeing & Lifestyle Benefits — Wellbeing support appears comparatively robust, highlighted by mental-health therapy/coaching sessions and broader wellness programming alongside community-oriented perks.
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The Company
What We Do
M&T Bank is a multi-state community-focused bank serving New York, Maryland, New Jersey, Pennsylvania, Delaware, Connecticut, Virginia, West Virginia and Washington, D.C. Founded in 1856, the company provides banking, investment, insurance and mortgage financial services to more than 3.6 million consumer, business and government clients.
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