Analytics Engineer

Job Description

Key Responsibilities

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

Core Qualifications & Requirements

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

Nice-to-Have Qualifications

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