Lead Technical Data Analyst

Raymond James Financial, Inc.

St. Petersburg, FL, United States

about 1 month ago

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Expert

Experience required

5 years minimum

Working hours

Regular working hours

Job source

www.techcareers.com

Tech stack

Artificial Intelligence Amazon Web Services Data Analysis Confluence JIRA Microsoft Azure Data Dictionary Information Engineering Data Integration Extract Transform Load (ETL) Data Mapping Data Warehousing

+9 more

Dimensional Modeling Microsoft SQL Server Oracle (Applications) Scrum Methodology SQL Databases Data Lakes Data Analytics Star Schema AWS Data Analytics

Job description

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., * 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.

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