Analytics Engineer

Key Responsibilities: Design, develop, and optimize data models, datasets, dashboards, and reports supporting monthly and quarterly fund performance and operational reporting cycles. Transform raw source data into high-quality, reusable, and structured datasets for analytics and business intelligence purposes. Utilize dimensional modeling principles to create and maintain fact and dimension tables, star schemas, and business metrics aligned with fund valuation and reporting needs. Write, test, and maintain complex SQL queries, stored procedures, and data transformations involving joins, aggregations, window functions, and subqueries across multiple systems. Develop analytics solutions using BI platforms such as Power BI, Sigma Computing, and Snowflake, including dashboard and report creation. Collaborate with Data Engineers to understand source system architecture, establish reliable data pipelines, resolve data-quality issues, and support end-to-end data workflows. Partner with Business Analysts and stakeholders, including fund managers and portfolio leads, to translate business requirements into effective data models, KPIs, and report deliverables. Validate data accuracy, reconcile discrepancies, and troubleshoot data issues across multiple systems and models. Implement and execute data quality tests, unit tests, and validation procedures to ensure the integrity of analytics outputs. Document data lineage, business logic, metrics definitions, and reporting processes thoroughly. Support deployment, monitoring, and maintenance of analytics solutions, ensuring continued performance and accuracy. Participate in code reviews, contribute to best practices, and help develop standards for analytics engineering within the organization. Core Qualifications & Requirements1 to 3 years of relevant experience in data analytics, data engineering, or reporting roles within finance, asset management, or private equity. Strong proficiency in SQL, including joins, aggregations, subqueries, window functions, and CTEs. Solid understanding of dimensional data modeling concepts such as fact/dimension tables, star schemas, and data relationships. Hands-on experience developing dashboards and reports using Power BI, Sigma Computing, Snowflake, or similar BI tools. Familiarity with data transformation frameworks like dbt or equivalent. Knowledge of source system integration, data pipeline development, and data quality assurance practices. Experience working with cloud data platforms such as Snowflake and AWS.Ability to collaborate effectively with Data Engineers on data pipelines and source system issues. Strong attention to detail, data validation, and troubleshooting skills. Excellent written and verbal communication, capable of translating technical concepts for stakeholders. Bachelor’s degree in Computer Science, Data Analytics, Finance, or related field, or equivalent practical experience.