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-end—across sourcing, transformation, curation, consumption, and run—and 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 data‑centric questions, identifying dependencies, risks, and constraints early.

2. Requirements for Data-Heavy Projects & Programs Lead end‑to‑end requirements for multi‑team, data‑intensive initiatives such as platform modernization and integrations.

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

3. Data Mapping, Modeling & Lifecycle

Drive source‑to‑target 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.