Data Analyst - Securities Lending

Senior Data Analyst - Securities Lending

We are seeking a Senior Data Analyst with strong expertise in Securities Lending or Capital Markets data. This pivotal role blends deep data analysis, financial domain knowledge, data modelling enablement, and engineering collaboration. The successful candidate will ensure canonical models accurately reflect real-world data, meet regulatory/business requirements, and are technically sound. This position serves as a crucial bridge across Business, Data Modelling, Engineering, and QA teams, ensuring end-to-end data integrity from initial source systems through to final data lake and reporting output.

Key Responsibilities

1. Data Analysis & Discovery Analyse diverse source systems (relational databases, flat files, vendor feeds). Conduct data profiling, pattern recognition, and field-level interpretation. Reverse-engineer data structures, business meaning, and transformation logic. Identify data quality issues, gaps, and inconsistencies.

2. Securities Lending Domain Expertise Decompose and analyze datasets pertinent to securities lending loans, collateral (cash/non-cash), investment positions/inventory, and accruals (e.g., rebates, fees, interest). Understand and document lifecycle events from trade initiation through settlement, collateralization, and return. Map relationships between accounts, counterparties, and securities/instruments.

3. Cross-Functional Collaboration Serve as the central integration point, collaborating with:

Business / SMEs: Validate data, clarify requirements, resolve ambiguities.

Data Modelers: Provide structured inputs (entities, attributes, relationships), ensuring model accuracy.

Engineering / Development Teams: Validate data pipelines and transformation logic.

Testing / QA Teams: Support test case design and validation, ensure end-to-end data correctness. Drive end-to-end traceability from raw data to business meaning and final outputs.

4. Data Alignment & Compliance Reconcile current state data from legacy systems against regulatory (SFTR, Basel/RWA), risk, finance, and compliance needs. Ensure alignment with industry standards (ISLA, ISO 20022) and cross-domain consistency.

5. Data Model Enablement Provide high-quality inputs for data modelling (data dictionaries, attribute definitions, entity relationships, source-to-canonical mappings). Define business rules, transformation logic, and validation rules. Ensure models are practical, implementable, and aligned with actual data behavior.

6. Technical Interpretation & Validation Read and interpret SQL queries and data transformation logic. Validate system implementation aligns with business and modelling intent, identifying and resolving gaps. Strong technical literacy is essential, though direct coding is not required.

7. Data Validation, Testing & Reconciliation Define validation frameworks and reconciliation rules. Investigate and resolve data breaks and mismatches across systems. Support issue resolution across data, modelling, and engineering layers.

8. AI-Assisted Data Analysis (Preferred) Leverage AI tools and agents for accelerated data exploration, profiling, mapping, and documentation to improve efficiency and depth of analysis.

Skills and Experience

Mandatory Requirements

Domain & Functional: Experience in Capital Markets data (Securities Lending strongly preferred). Strong understanding of at least one of the following: Positions / inventory Collateral / margining Trade lifecycle

Data & Technical: Strong SQL and hands-on data analysis capability. Experience working with relational databases (e.g., Oracle), large/complex datasets, and flat files/vendor feeds. Ability to read and interpret SQL and data transformation logic.

Core Capability: Strong analytical and problem-solving skills, including the ability to: Interpret complex datasets. Identify patterns, inconsistencies, and gaps. Derive business meaning from raw data.

Collaboration: Proven experience working with Business stakeholders, Data modelers, Developers, and Testing / QA teams. Ability to drive end-to-end traceability from data to output.

Preferred Requirements

Domain Depth: Direct experience in Securities Lending.

Data Modelling Awareness: Understanding of canonical data models, logical vs. physical modeling, and entity relationships.

Regulatory & Standards: Exposure to SFTR, Basel / RWA concepts. Familiarity with ISLA, ISO 20022.

Technical Environment: Experience with Java, Oracle, and Hadoop tech stack. Exposure to data pipelines / ETL tools.

AI / Modern Tooling: Exposure to AI tools or agents for data analysis, documentation, and mapping.

Soft Skills (Mandatory) Excellent communication and documentation skills. Ability to challenge assumptions and validate logic. Ability to operate effectively across business and technical teams.

Diverse Lynx LLC is an Equal Employment Opportunity employer. All qualified applicants will receive due consideration for employment without any discrimination. All applicants will be evaluated solely on the basis of their ability, competence and their proven capability to perform the functions outlined in the corresponding role. We promote and support a diverse workforce across all levels in the company.