Data Analyst – Banking Regulatory Reporting

Job Description: Role: Technology/Data Analyst

100% Remote

Primary Skills: Strong understanding of data concepts (entities relationships keys domains) data modeling patterns (relational dimensional lakehouse) and data lifecycle across environments.

- Practical experience interpreting and specifying data flows data contracts and interface requirements between systems and platforms.

- Familiarity with SQL and/or other data analysis tools sufficient to validate assumptions mappings and edge cases with rea

The Technology Analyst (Data Focus) is a senior individual contributor who combines strong technology analysis skills with deep data expertise to shape and deliver large data-intensive projects and programs. This role partners with business leaders product owners and engineering teams to translate complex multi-domain business outcomes into secure scalable solution requirements for initiatives that are heavy in data movement mapping and modeling. The analyst understands data lifecycle end-to-endacross sourcing transformation curation consumption and runand ensures requirements reflect how data flows changes and is governed across projects and platforms

1. Business & Data Outcomes Definition

Partner with business and product leaders to define strategic objectives KPIs and regulatory or reporting needs.

Break down complex outcomes into datacentric questions identifying dependencies risks and constraints early.

2. Requirements for Data-Heavy Projects & Programs Lead endtoend requirements for multiteam dataintensive initiatives such as platform modernization and integrations.

Translate business outcomes into clear functional and nonfunctional data requirements aligned to quality and governance standards.

3. Data Mapping Modeling & Lifecycle

Drive sourcetotarget data mapping across legacy SaaS and cloud platforms documenting transformations and standards.

Ensure requirements support the full data lifecycle including lineage retention controls and downstream impact.

4. Secure Reliable Data Solutioning

Embed secure engineering practices and define requirements for data quality validation and monitoring.

Partner with risk governance and architecture teams to meet trusted data privacy and regulatory expectations.