Join our Technology & Transformation practice and unlock the immense value hidden within vast datasets. We offer strategic guidance and implementation services to help companies expertly manage data from diverse sources. Our goal is to transform this data into precise, actionable insights, empowering data-driven decision-making and creating a distinct competitive advantage.

Our practice encompasses a broad spectrum of opportunities, including business intelligence and visualization, comprehensive data management, performance management, and cutting-edge analytics technologies such as big data, cloud, cognitive computing, and machine learning.

Develop robust conceptual, logical, and physical data models for enterprise data warehouses and cloud data platforms.

Design and implement effective dimensional modeling patterns, including facts, dimensions, conformed dimensions, bridge tables, hierarchies, and SCD Type 1/Type 2.

Create detailed source-to-target mappings, data dictionaries, attribute definitions, lineage documentation, and establish modeling standards.

Leverage strong knowledge of SQL, ETL/ELT processes, dbt transformation patterns, data quality frameworks, reconciliation techniques, and analytics consumption layers.

Support complexity assessments, conduct model reviews, facilitate engineering handovers, and produce comprehensive documentation for downstream data engineering and reporting teams.

Bring extensive experience in Asset Management or Private Markets data models, specifically covering Fund, Vehicle, Investor, Asset, Position, Transaction, NAV, GL, Cashflow, Fee, and Performance datasets.

Demonstrate a solid understanding of ABOR and PBOR concepts, including fund accounting, capital activity, valuation, investor reporting, IRR, TVPI, DPI, RVPI, track record, and fee calculations.

Possess practical experience translating fund data, source structures, business rules, and reporting requirements into scalable Data Mart models.

Showcase the ability to collaborate effectively with business SMEs, Data Managers, and delivery teams to define entity relationships, table granularity, hierarchies, and reusable mart patterns.

Experience with platforms like GCP BigQuery, Snowflake, Databricks, or Azure Synapse is highly preferred. Familiarity with Airflow-based orchestration and the operationalization of data models through scheduled pipelines is also beneficial.