Analyst

Role: Analyst

Location: Rockville, MD or Tysons, VA (3 days onsite in a week) (Need local only)

Business Analyst with heavy SQL or Data/Business analyst (hybrid) could work. Needs both business analysis experience(gap analysis, UAT, etc.) and strong SQL.

Must have’s:

• Business analysis experience (gap analysis, UAT, etc.)
• SQL – must on large scale datasets (on day to day – 20% to 30% in data analysis, answering questions, able to write SQL queries with some windows function knowledge)
• Ideal: 5+ years of financial regulatory, surveillance or capital markets data analyst who will be analyzing, validating surveillance patterns, documenting the related business rules, etc.

Nice to have’s:

• GenAI

JD: The Analyst conducts moderate-to-high complexity analysis of automated surveillance systems that detect potential violations of regulatory rules. This role is responsible for understanding, validating, enhancing, and supporting surveillance patterns across multiple asset classes and regulatory domains. The analyst serves as the bridge between regulatory requirements and the technical implementation of surveillance logic, ensuring patterns produce accurate, complete, and defensible results. The analyst is expected to actively leverage generative AI tools to accelerate analysis, documentation, and automation — serving as the domain expert who directs AI capabilities while ensuring regulatory accuracy and completeness of all outputs.

CORE FUNCTIONS

Surveillance Pattern Analysis & Knowledge Management

- Reverse-engineers surveillance pattern source code to build comprehensive knowledge documentation covering data pipelines, business rules, exception logic, and output schemas

- Maintains a knowledge base of surveillance patterns across multiple regulatory domains

- Performs requirements-vs-code gap analysis to identify discrepancies between documented business requirements and implemented logic, flagging risks to surveillance coverage

UAT & Release Validation

- Validates new surveillance pattern releases by comparing production vs. UAT outputs, analyzing exception counts, volumes, and firm-level impacts

- Builds local testing environments to execute query scripts against sample data, enabling independent verification of pattern logic before production deployment

- Designs multi-scenario test cases covering edge cases to ensure comprehensive test coverage

- Packages release artifacts (scripts, QC results, requirements documentation) following standardized release structures

Ad-Hoc Data Analysis & Investigation Support

- Writes complex queries to support analyst investigations into surveillance exceptions

- Performs firm-level statistical analysis and produces reports for management review

- Maintains reference data sets used by downstream surveillance patterns

- Assesses data incident impacts by cross-referencing affected data sources against the surveillance pattern dependency map

Process Automation & Tooling

- Develops automation tools for ticket management, release documentation, and workflow optimization

- Builds local testing frameworks that replicate production environments for rapid pattern testing and iteration

- Creates data visualization and comparison reports with interactive views, summary statistics, and color-coded differentials

Generative AI Integration & Prompt Engineering

- Leverages AI coding assistants as a force multiplier — directing AI agents to reverse-engineer source code, build knowledge documentation, generate test frameworks, and produce data comparison reports

- Designs structured AI workflows including pattern study pipelines, workflow assistants, and automated documentation generation

- Maintains a curated AI project workspace with standing instructions, conventions, and context files that enable consistent, high-quality AI-assisted output

- Coordinates multi-step analytical tasks through AI — from reading source code and drafting requirements, to generating test harnesses and producing comparison reports — reducing turnaround from days to hours

- Evaluates and validates AI-generated outputs for regulatory accuracy before use in production workflows

Project Management Support

- Manages work across project tracking boards, tracking enhancement tickets, bug fixes, and new pattern development

- Drafts and updates Business Requirements Documents (BRDs) for surveillance pattern enhancements

- Coordinates release activities including documentation, QC, and UAT sign-off tracking

OTHER RESPONSIBILITIES

Participates on special projects as assigned. Serves as a mentor for less experienced analysts. Keeps management updated on notable trends in industry practices, regulatory changes, and emerging technology capabilities (including AI tooling). Champions adoption of AI-assisted workflows within the team.

QUALIFICATIONS

- Bachelor's degree and 5+ years of experience in financial regulatory technology, surveillance systems, or capital markets data analysis

- Proficiency in SQL for large-scale data analysis on distributed platforms

- Proficiency in at least one scripting/programming language for data analysis, automation, and tool development

- Experience reading and interpreting complex data processing pipelines and business logic in source code

- Strong analytical skills with the ability to independently investigate data discrepancies and validate system outputs

- Excellent documentation skills — ability to translate complex technical implementations into clear business-facing knowledge documents

- Advanced proficiency in spreadsheet tools for business and operational analysis

Preferred:

- Major in Business Administration, Finance, Computer Science, or related field; advanced degree preferred

- Experience with distributed query engines and big data platforms

- Experience with project management and documentation tools and their APIs

- Familiarity with surveillance pattern architectures (exception generation, scoring, aggregation, reconciliation)

- Experience with source code management and release branch workflows

- Experience leveraging generative AI tools (LLMs, AI coding assistants) to accelerate data analysis, code comprehension, documentation, and automation workflows

- Ability to design effective AI prompts and structured project contexts that produce reliable, domain-accurate outputs in a regulated environment