Senior Data Engineer

M&T Bank•Wilmington, DE

1d•$97,100 - $161,800

About The Position

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. The role partners with product owners, architects, governance, cybersecurity, platform, and consuming teams to clarify requirements and deliver supported solutions. It contributes to technical design and code reviews, Agile delivery, production support, incident resolution, and root-cause analysis. The position also provides day-to-day technical assistance and informal mentoring to less-experienced engineers, without direct people-management responsibility. The role is expected to follow the Company’s risk and regulatory standards, policies, and controls in accordance with its Risk Appetite; maintain and enhance internal controls; escalate risk-related issues; and address applicable audit and regulatory findings. Other related duties may be assigned.

Requirements

Associate’s degree and a minimum of 5 years of systems analysis and/or application development work experience; or a bachelor’s degree and a minimum of 3 years of such experience; or, in lieu of a degree, a combined minimum of 7 years of education and/or relevant work experience, including at least 3 years of 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 formats such as 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 while identifying opportunities for improvement.
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 day-to-day supervision.

Nice To Haves

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.

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 supporting 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; the role has no 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 applicable internal and external audit points and issues raised by external regulators.
Promote an environment that supports belonging and reflects the Company brand.
Complete other related duties as assigned.