Business Intelligence Engineer

Job Description

Key Responsibilities

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• Design, develop, and optimize data models, datasets, dashboards, and reports supporting monthly and quarterly fund performance and operational reporting cycles.
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• Transform raw source data into high-quality, reusable, and structured datasets for analytics and business intelligence purposes.
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• Utilize dimensional modeling principles to create and maintain fact and dimension tables, star schemas, and business metrics aligned with fund valuation and reporting needs.
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• Write, test, and maintain complex SQL queries, stored procedures, and data transformations involving joins, aggregations, window functions, and subqueries across multiple systems.
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• Develop analytics solutions using BI platforms such as Power BI, Sigma Computing, and Snowflake, including dashboard and report creation.
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• Collaborate with Data Engineers to understand source system architecture, establish reliable data pipelines, resolve data-quality issues, and support end-to-end data workflows.
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• Partner with Business Analysts and stakeholders, including fund managers and portfolio leads, to translate business requirements into effective data models, KPIs, and report deliverables.
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• Validate data accuracy, reconcile discrepancies, and troubleshoot data issues across multiple systems and models.
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• Implement and execute data quality tests, unit tests, and validation procedures to ensure the integrity of analytics outputs.
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• Document data lineage, business logic, metrics definitions, and reporting processes thoroughly.
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• Support deployment, monitoring, and maintenance of analytics solutions, ensuring continued performance and accuracy.
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• Participate in code reviews, contribute to best practices, and help develop standards for analytics engineering within the organization.
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Core Qualifications & Requirements

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• 1 to 3 years of relevant experience in data analytics, data engineering, or reporting roles within finance, asset management, or private equity.
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• Strong proficiency in SQL, including joins, aggregations, subqueries, window functions, and CTEs.
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• Solid understanding of dimensional data modeling concepts such as fact/dimension tables, star schemas, and data relationships.
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• Hands-on experience developing dashboards and reports using Power BI, Sigma Computing, Snowflake, or similar BI tools.
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• Familiarity with data transformation frameworks like dbt or equivalent.
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• Knowledge of source system integration, data pipeline development, and data quality assurance practices.
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• Experience working with cloud data platforms such as Snowflake and AWS.
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• Ability to collaborate effectively with Data Engineers on data pipelines and source system issues.
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• Strong attention to detail, data validation, and troubleshooting skills.
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• Excellent written and verbal communication, capable of translating technical concepts for stakeholders.
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• Bachelor’s degree in Computer Science, Data Analytics, Finance, or related field, or equivalent practical experience.
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Nice-to-Have Qualifications

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• Exposure to Sigma Computing, Power BI, and Snowflake.
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• Experience with data orchestration tools like Airflow or DataHub.
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• Familiarity with data quality automation and lineage documentation.
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• Knowledge of finance, private equity, or real estate operational processes.
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• Understanding of software development best practices such as modular design, version control, and deployment workflows.
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• Comfortable working within Agile project methodologies.
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Core Technical Skills

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• SQL (joins, filters, aggregations, CTEs, window functions)
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• Dimensional data modeling (fact/dimension tables, star schemas)
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• Business Intelligence tools (Power BI, Sigma Computing)
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• Cloud data platforms (Snowflake, AWS)
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• Data pipeline/tools (dbt, Airflow, DataHub)
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• Data testing and validation practices
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• Version control (Git)
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