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.