Role Overview

We are seeking a Business Analyst to support a large-scale data transformation program within the investment management and advisory domain. The role requires a strong functional understanding of investment data (public and private markets), combined with hands-on data analysis capabilities, including source-to-target mapping and SQL-based exploration. The analyst will act as a bridge between business stakeholders, data engineering teams, and platform architects to ensure accurate, high-quality data delivery aligned with business needs.

Key Responsibilities

Collaborate with business stakeholders (investment teams, research, performance reporting, client reporting) to gather, analyze, and document data requirements.
Translate business requirements into detailed functional specifications, including source-to-target mappings (STTM) for data migration and transformation initiatives.
Analyze source systems and data structures to understand data lineage, quality issues, and transformation logic.
Write and execute SQL queries for data profiling, validation, reconciliation, and root cause analysis.
Work closely with data engineering teams (ETL/ELT, Snowflake/Databricks environments) to ensure correct implementation of business rules.
Support data quality initiatives by defining validation rules, reconciliation logic, and exception handling processes.
Participate in data model reviews, ensuring alignment with investment domain concepts such as portfolios, securities, benchmarks, transactions, and performance metrics.
Assist in UAT planning and execution, including test case creation, defect tracking, and validation of transformed datasets.
Contribute to data governance practices, including metadata documentation, data lineage tracking, and glossary definitions.
Engage in Agile ceremonies and provide continuous feedback to improve delivery outcomes.

Required Skills And Experience

Strong understanding of investment management domain concepts, including:
Public markets (equities, fixed income)
Private investments (PE, VC, real assets)
Portfolio structures, holdings, transactions, benchmarks
Performance and attribution concepts

Other Experiences

Proven experience in data transformation or data migration programs within financial services or asset management.
Hands-on experience in creating source-to-target mappings and functional specifications for data pipelines
Strong SQL skills for data analysis, including joins, aggregations, window functions, and data validation queries.
Experience working with modern data platforms such as Snowflake, Databricks, or similar cloud-based ecosystems.
Familiarity with data warehousing concepts, dimensional modeling, and data lake architectures
Strong analytical thinking and ability to interpret complex datasets and business rules.
Experience working in Agile delivery environments.

Preferred Qualifications

Exposure to tools/platforms commonly used in investment data ecosystems (e.g., Bloomberg, FactSet, Aladdin, Burgiss, or similar).
Understanding of data governance frameworks, including data quality, lineage, and stewardship.
Experience with ETL/ELT tools or orchestration frameworks (e.g., Airflow).
Basic understanding of Python/PySpark for data analysis is a plus.
Prior experience in working with consulting firms or investment advisors is highly desirable.

Key Competencies

Strong communication skills with the ability to interact with both business and technical teams.
Attention to detail, especially in data mapping and validation activities.
Problem-solving mindset with the ability to identify data issues and propose solutions.
Ability to manage multiple priorities in a fast-paced transformation environment.

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