Participate in Agile planning, backlog refinement, estimation, design discussions, sprint execution, and retrospectives.
Develop scalable and reusable data models, transformations, and integration patterns.
Support analysis and modernization of legacy ETL and file-based data flows.
Implement data testing, reconciliation, monitoring, alerting, and exception-handling controls.
Design reliable processing to support 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 for analysis, coding, testing, troubleshooting, and documentation while validating generated output.
Participate in a 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.
Experience Requirements
Hands-on experience with Snowflake, dbt, and SQL.
Experience designing, implementing, and supporting production data flows.
Ability to translate business problems into technical requirements and implementable solutions.
Experience working through ambiguous or evolving requirements.
Experience refining 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, problem-solving, documentation, and communication skills.
Ability to work independently while collaborating with business and technical partners.
Willingness to participate in a shared production-support rotation.
Nice to have: legacy ETL (DataStage, Informatica, or similar), Python, CI/CD, TWS or comparable scheduling, treasury or financial-risk data experience, QRM or Oracle OFSA/OFSAA/FTPCS, SaaS integrations via APIs or batch files, financial data reconciliation to general-ledger balances, modernizing legacy ETL with Snowflake and dbt.
Education Requirements
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.
