Data Engineer

MINIMUM QUALIFICATIONS
Bachelor’s degree in Computer Science, Information Systems, Engineering, or an equivalent combination of education and experience
Typically three or more years of data-engineering, ETL/ELT, or comparable software-engineering experience
Demonstrated experience contributing to data solutions from problem definition through production implementation and support

GENERAL FUNCTION
The Data Engineer designs, builds, and supports scalable, production-ready data solutions using Snowflake, dbt, SQL, and related enterprise technologies. A critical responsibility of this role is understanding business problems and translating them into clearly defined technical requirements and implementable data solutions. The engineer must be comfortable working through ambiguity, understanding existing business processes and legacy data flows, and contributing throughout discovery, design, implementation, testing, deployment, and ongoing production support. This role will support the modernization of financial data flows, including integrations with QRM and Oracle OFSAA/FTPCS. The engineer is expected to use approved AI-assisted development tools, such as GitHub Copilot, to improve delivery speed, accuracy, testing, documentation, and maintainability while independently reviewing and validating generated output.

ESSENTIAL DUTIES AND RESPONSIBILITIES
Partner with business and technical stakeholders to understand business problems and translate them into clearly defined technical requirements and data solutions.
Analyze business processes, source data, existing system behavior, and downstream usage to identify the underlying technical problem to be solved.
Design, build, test, deploy, and support production data pipelines using Snowflake, dbt, SQL, and approved enterprise technologies.
Refine high-level features into workable Agile stories with clear technical requirements and acceptance criteria.
Participate in Agile planning, backlog refinement, estimation, design discussions, sprint execution, and retrospectives.
Develop scalable and reusable data models, transformations, and integration patterns.
Support the analysis and modernization of legacy ETL and file-based data flows.
Implement appropriate data testing, reconciliation, monitoring, alerting, and exception-handling controls.
Design reliable processing that supports reruns, source corrections, backfills, and recovery from production failures.
Integrate data pipelines with databases, schedulers, APIs, files, and vendor applications.
Use approved AI-assisted development tools to improve analysis, coding, testing, troubleshooting, and documentation while validating generated output.
Participate in the team’s shared production-support rotation and assist with critical data-processing incidents.
Troubleshoot production issues, perform root-cause analysis, and implement sustainable corrections.
Maintain technical designs, source-to-target mappings, data-flow documentation, runbooks, and troubleshooting procedures.
Identify opportunities to simplify processing, automate recurring activities, and improve operational stability.

MUST-HAVE SKILLS AND EXPERIENCE
Hands-on experience with Snowflake, dbt, and SQL
Experience designing, implementing, and supporting production data flows
Proven ability to translate business problems into technical requirements and implementable solutions
Experience working through ambiguous, incomplete, or evolving requirements
Experience refining high-level features into workable stories within an Agile team
Experience with data modeling, testing, reconciliation, and production troubleshooting
Understanding of reruns, backfills, failure recovery, and operational supportability
Experience with Git-based development and code-review practices
Ability to analyze and modernize complex or poorly documented data flows
Strong analytical, critical-thinking, problem-solving, documentation, and communication skills
Ability to work independently while collaborating effectively with business and technical partners
Willingness to participate in a shared production-support rotation

NICE-TO-HAVE SKILLS AND EXPERIENCE
Experience with legacy ETL platforms such as DataStage, Informatica, or comparable technologies
Python and CI/CD experience
Experience using AI-assisted development tools such as GitHub Copilot
TWS or comparable enterprise scheduling experience
Treasury, liquidity, asset-liability management, funds transfer pricing, financial-risk, or regulatory-data experience
QRM, Oracle OFSA/OFSAA, or Oracle FTPCS experience
Experience integrating SaaS or vendor-hosted applications through APIs or batch files
Experience reconciling financial data to general-ledger balances
Experience modernizing legacy ETL solutions using Snowflake and dbt