What are the responsibilities and job description for the Business Analyst position at QODE?
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
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.
- 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
- 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.
- 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.
- 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.