Data Engineer III (US)

Work Location:

Mount Laurel, New Jersey, United States of America

Hours:

40

Pay Details:

$98,160 - $159,270 USD

TD is committed to providing fair and equitable compensation opportunities to all colleagues. Growth opportunities and skill development are defining features of the colleague experience at TD. Our compensation policies and practices have been designed to allow colleagues to progress through the salary range over time as they progress in their role. The base pay actually offered may vary based upon the candidate's skills and experience, job-related knowledge, geographic location, and other specific business and organizational needs.

As a candidate, you are encouraged to ask compensation related questions and have an open dialogue with your recruiter who can provide you more specific details for this role.

Line of Business:

Technology Solutions

Job Description:

The Data Engineer III provides a broad range of data engineering functions including data modeling, data quality, data profiling, data acquisition and ingestion, extract transform load (ETL), metadata enrichment and management, data provenance and lineage, and other specialized data management functions.

Depth & Scope:

* Expert knowledge of data engineering frameworks, technologies, tools, processes, patterns, and procedures, including how they impact and integrate with other technology areas such as Architecture or Infrastructure
* Performs highly complex technical tasks independently
* Expert knowledge of TD applications, systems, networks, innovation, design activities, business, organization, best practices, and standards
* Designs and develops to meet business and technical requirements; analyzes, adapts, integrates, codes, tests, debugs, and executes
* Uses and evolves established patterns to solve complex problems; leads the development of new patterns where necessary
* Recognized as primary subject matter expert in multiple areas directly and indirectly related to key accountabilities
* Consults with peers and partners across multiple teams on all aspects of research, analysis, design, and support

Education & Experience:

* Degree, Postgraduate Degree, or Technical Certificate in Data Management or related discipline (e.g. Computer Science, Engineering), or equivalent practical experience
* Graduate Degree preferred
* 5+ years of relevant experience

Preferred Qualifications:

5+ years of experience with the following:

* Azure Databricks: Hands-on experience designing, developing, optimizing, and supporting notebooks, transformations, and scalable data-processing workloads in enterprise environments.
* PySpark: Strong proficiency designing distributed data transformations and processing large-scale datasets using Apache Spark, including performance optimization and troubleshooting.
* SQL: Advanced ability to write, optimize, and troubleshoot complex queries, transformations, data-validation logic, and performance-intensive workloads.
* Azure Data Factory (ADF): Experience designing, developing, scheduling, monitoring, and troubleshooting enterprise-grade ingestion and orchestration pipelines.
* Big Data, ETL, and Data Engineering: Strong understanding of data-integration patterns, batch and streaming processing, pipeline design, performance, scalability, reliability, and production support.
* GitHub or Bitbucket: Proficiency with source control, branching strategies, pull requests, code reviews, versioning, and collaborative engineering practices.
* Analytical and problem-solving skills: Demonstrated ability to investigate complex data and application issues, perform root-cause analysis, evaluate solution options, and implement sustainable solutions.

3+ years of experience with the following:

* Delta Lake: Experience implementing reliable lakehouse solutions, including table design, schema management, data versioning, performance optimization, and scalable data operations.
* Unity Catalog: Experience with centralized data governance, access controls, metadata management, lineage, and data discovery across Databricks environments.
* CI/CD and DevOps practices: Experience designing or implementing automated build, test, deployment, and promotion processes for data pipelines, notebooks, and related data-engineering components.
* Data governance and lineage: Experience applying metadata management, data catalogs, lineage, data-quality controls, critical data elements, and enterprise governance standards.
* Cloud migration and modernization: Experience delivering initiatives that migrate or modernize legacy data platforms, pipelines, and workloads to cloud-based architectures.

2+ years of technical leadership experience:

* Lead / Tech Lead / Senior Engineer: Demonstrated experience operating as a Lead Engineer, Technical Lead, Senior Engineer, or equivalent senior technical role, providing technical direction and guidance to engineering teams.
* Experience leading solution design, technical implementation, code reviews, engineering standards, troubleshooting, and production-readiness activities.
* Ability to mentor engineers, drive engineering best practices, coordinate across technical and business teams, and take ownership of complex deliverables from design through production.

Nice to Have:

* 2+ years of Data Product development experience: Experience designing, building, and supporting reusable, governed, and production-grade Data Products for consumption by analytics, reporting, applications, or downstream data consumers.
* Experience defining and implementing data contracts, quality expectations, metadata, lineage, SLAs/SLOs, and ownership models for Data Products.
* Experience building Data Products using modern cloud and lakehouse technologies such as Azure Databricks, Delta Lake, Unity Catalog, PySpark, and Azure Data Factory.

Customer Accountabilities:

* Performs data analysis and assesses data management requirements for a specific Platform or Journey, including complex analysis involving multiple pods or products
* Maintains expert knowledge of upstream data, including knowledge provided through data profiling, data quality reporting, and via the production of metadata
* Supports the acquisition and ingestion of data
* Articulates complex, large scale, and high impact technical design and development details to non-technical business partners
* Elicits, analyzes, and understands business and data requirements to develop complete business solutions, including data models (entity relationship diagrams, dimensional data models), ETL and business rules, data life-cycle management, governance, lineage, and metadata
* Ensures data is maintained in compliance with enterprise data standards, policies, and guidelines
* Develops and maintains complex data models using industry standard modeling tools
* Develops and maintains complex ETL jobs and frameworks using the Bank's standard tools
* Provides support to the development and testing teams to resolve data issues, including escalation support on complex issues
* Supports partners and stakeholders in interpreting and analyzing data
* Builds effective working relationships within own pod and across partner teams to encourage collaboration on all pod deliverables

Shareholder Accountabilities:

* Coordinates with technology work teams such as ITS, ARE, Architecture, Enterprise Protect etc. to ensure overall delivery success
* Supports the QA team with data analysis/investigations of complex issues/ test cases as part of SIT/UAT/PAT testing
* Provides oversight on post implementation activities during the warranty period
* Executes & approves code check-in/ check-out into source code repository as part of source code management
* Works closely with ITS/ ARE teams to support code packaging & deployment (CI & CD) into higher environments
* Is the lead participant in the design & architecture reviews or the application
* Raises service-now requests and works with the change management team to support release m

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