Lead Technical Data Analyst

Raymond James•Memphis, FL

23h•Hybrid

About The Position

This position follows a hybrid work model, with an expectation to be in the office 3 days per week at the St. Petersburg, FL Corporate Office location. This role is not eligible for Work Visa sponsorship, either currently or in the future. We need a Lead Technical Data Analyst who can own the requirements and data-mapping side of our AWS data platform. You do not need to be an expert in every AWS service day one; you need strong business-analysis fundamentals, solid SQL, and a good grasp of how data warehouses and data lakes work. You will work with engineers, risk analysts, and platform teams to turn business needs into clear, testable requirements. You will be part of a team that is augmenting business/data analysis with AI directly inside the IDE. We use Claude models to accelerate the hard parts of the job: mapping data gaps, drafting requirements and user stories, generating test cases, analyzing datasets, and helping engineers review code and outputs. You will spend less time on boilerplate and more time on the judgment, domain expertise, and stakeholder work that actually matters.

Requirements

5+ years as a business analyst in data, BI, reporting, or data-integration projects.
Strong understanding of data warehouse concepts: ETL/ELT, dimensional modeling, star schema, slowly changing dimensions, fact vs. dimension tables.
Solid SQL; comfortable reading and validating queries across Oracle, SQL Server, or cloud query engines.
Familiarity with one or more AWS data services.
Experience writing acceptance criteria, source-to-target mappings, and data dictionaries.
Experience with Agile/Scrum, ADO or Jira, and Confluence.
Strong communication, documentation, and stakeholder-management skills.
Curiosity about using AI assistants to raise the quality and speed of analysis.

Nice To Haves

Experience in financial services or risk/data analytics is a plus.

Responsibilities

Elicit and document business and data requirements as user stories, PBIs, BRDs, and acceptance criteria.
Build source-to-target mappings and data dictionaries for Oracle, SQL Server, vendor files, and cloud data products.
Define data quality and reconciliation rules with engineering and make sure they are testable.
Support dimensional modeling decisions—facts, dimensions, SCD Type 1/2, and table classifications such as temporal, append-only, and snapshot.
Validate delivered data products against requirements and support UAT.
Coordinate across risk, data engineering, and platform teams; keep requirements and documentation organized in ADO / Confluence.
Help shape the backlog and sprint priorities for the BA workstream.
Use AI tooling to speed up mapping, requirements drafting, test-case generation, data analysis, and review tasks.