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Data/AI Engineer III
TD. Perform data modeling, data quality, data profiling, data acquisition and ingestion, ETL, metadata enrichment and management, data provenance and lineage, and specialized data management functions .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in data engineering, including data modeling, ETL development, and data governance, while leveraging advanced skills in Python, SQL, and Azure technologies. Proven ability to design and implement solutions that meet complex business and technical requirements in highly regulated environments.
Highest-signal resume keywords
Data Engineering Using AzureAdvanced Proficiency In PythonETL Development And FrameworksData Governance And Metadata ManagementExperience In Financial Services
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
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Hard Skills
Data ModelingETLData QualityData ProfilingData AcquisitionData IngestionBatch ProcessingStreamingDimensional ModelingGenAI Development
Soft Skills
MentoringCollaborationProblem-SolvingCommunicationStakeholder Management
Tools & Technologies
DatabricksSpark/PySparkMLflowServiceNowLakehouse Architecture
Certifications & Qualifications
Degree In Data ManagementPostgraduate Degree In Computer ScienceTechnical Certificate In Engineering
Industry Keywords
Financial ServicesHighly Regulated IndustryData Governance PrinciplesData Lifecycle ManagementEnterprise Standards
Tech Stack
Tools & technologiesAzureETLPySparkPythonServiceNowSparkSQL
About the role
Key responsibilities & impact- Perform data modeling, data quality, data profiling, data acquisition and ingestion, ETL, metadata enrichment and management, data provenance and lineage, and specialized data management functions
- Perform complex data analysis and assess data management requirements for platforms or journeys
- Maintain expert knowledge of upstream data through profiling, data quality reporting, and metadata production
- Design and develop solutions meeting business and technical requirements
- Develop and maintain complex data models using industry-standard modeling tools
- Develop and maintain complex ETL jobs and frameworks using the bank’s standard tools
- Elicit and analyze business and data requirements to develop complete solutions, including data models, ETL, business rules, lifecycle management, governance, lineage, and metadata
- Ensure data complies with enterprise standards, policies, and guidelines
- Support development and testing teams in resolving data issues, including escalation support
- Support partners and stakeholders in interpreting and analyzing data
- Consult with peers and partners across multiple teams on research, analysis, design, and support
- Coordinate with technology work teams including ITS, ARE, Architecture, and Enterprise Protect
- Support QA investigations and complex test cases during SIT/UAT/PAT testing
- Provide oversight during post-implementation warranty activities
- Execute and approve code check-in/check-out and support CI/CD packaging and deployment
- Lead participation in application design and architecture reviews
- Raise ServiceNow requests and support change and release management
- Lead data engineering initiatives and apply data governance principles across the organization
- Capture metadata and data lineage compatibly with enterprise tools and processes
- Identify and document technical decisions, risks, and lessons learned
- Keep current on emerging trends and developments in data engineering
- Share knowledge, mentor and enable team members, and provide guidance
- Build productive relationships with internal and external partners and stakeholders
- Participate in performance management, development activities, cross-training, and project-status communications
- Protect organizational interests by identifying, managing, and escalating risks
Requirements
What you’ll need- Degree, Postgraduate Degree, or Technical Certificate in Data Management or a related discipline, such as Computer Science or Engineering, or equivalent practical experience
- 5+ years of relevant experience
- 5+ years of experience with strong hands-on data engineering using Azure and Databricks
- Advanced proficiency in Python, SQL, and Spark/PySpark
- Understanding of batch processing, streaming, dimensional modeling, Lakehouse architecture, and distributed processing
- Hands-on experience developing GenAI or agentic applications
- Experience with Databricks Genie, AI/BI, MLflow, vector search, RAG, or comparable technologies
- Experience engineering data for AI/ML workloads
- Experience within financial services or another highly regulated industry
- Expert knowledge of data engineering frameworks, technologies, tools, processes, patterns, and procedures
- Expert knowledge of TD applications, systems, networks, innovation, design activities, business, organization, best practices, and standards
- Ability to perform highly complex technical tasks independently
- Ability to design, develop, analyze, adapt, integrate, code, test, debug, and execute solutions
- Ability to develop and maintain complex data models and ETL jobs/frameworks
- Knowledge of data governance, metadata, data lineage, data quality, data profiling, data acquisition, ingestion, and data lifecycle management
- Ability to support QA, SIT/UAT/PAT testing, deployment, release management, and source code management
- Ability to adhere to security coding practices and regulatory guidelines
Benefits
Comp & perks- Variable compensation/incentive awards, including eligibility for cash and/or equity incentive awards
- Health and well-being benefits
- Savings and retirement programs
- Paid time off, including Vacation PTO, Flex PTO, and Holiday PTO
- Banking benefits and discounts
- Career development
- Reward and recognition
- Regular career, development, and performance conversations
- Online learning platform
- Mentoring programs
- Training and onboarding sessions
- Domestic travel opportunities
- Disability accommodations during the application process