Business Intelligence Engineer - Remote Position

The Junior Analytics Engineer will be an integral member of the Data Analytics and Insights team, developing and maintaining data models, analytics solutions, and interactive reporting applications that support decision-making across private real estate investment funds. This role will focus on leveraging Sigma Computing and Snowflake to transform complex financial and operational data into scalable, user-friendly analytics solutions. The ideal candidate has a strong foundation in SQL, data modeling, and business intelligence, with an interest in bridging data engineering and business-facing analytics. Working closely with Data Engineers, Business Analysts, and investment stakeholders, this individual will help build reliable datasets, streamline reporting workflows, and deliver actionable insights across fund performance and operations. Develop and maintain interactive dashboards, reports, and data applications primarily using Sigma Computing , supporting monthly and quarterly fund performance and operational reporting. Build, optimize, and maintain SQL-based datasets and data models in Snowflake to support reporting, analytics, and business intelligence. Transform raw source data into clean, structured, reusable datasets that support consistent reporting and analysis. Apply dimensional modeling principles to design fact and dimension tables, star schemas, and business metrics aligned with fund valuation and reporting requirements. Write and optimize complex SQL queries using joins, aggregations, CTEs, subqueries, and window functions across multiple data sources. Partner with Data Engineers to understand source systems, troubleshoot data pipeline issues, and ensure reliable data availability for downstream analytics. Collaborate with Business Analysts, fund managers, and portfolio teams to translate business requirements into effective data models, KPIs, and Sigma-based reporting solutions. Validate data accuracy, reconcile discrepancies, and troubleshoot inconsistencies across datasets, dashboards, and reporting outputs. Implement data quality checks, testing procedures, and validation processes to maintain the integrity of analytics solutions. Document data models, business logic, metric definitions, and reporting workflows to promote consistency and maintainability. Support the deployment, monitoring, and ongoing enhancement of analytics solutions, ensuring performance, scalability, and accuracy. 1–3 years of experience in data analytics, analytics engineering, business intelligence, or data engineering. ~ Hands-on experience with Sigma Computing or a comparable modern BI platform, with a strong interest in building interactive, data-driven reporting solutions. ~ Strong SQL proficiency, including joins, aggregations, CTEs, subqueries, and window functions. ~ Experience working with Snowflake or another cloud-based data warehouse. ~ Solid understanding of dimensional data modeling, including fact and dimension tables, star schemas, and data relationships. ~ Experience developing dashboards, reports, or analytical applications that support business decision-making. ~ Familiarity with data transformation frameworks such as dbt or equivalent tools. ~ Understanding of data pipelines, source system integration, and data quality assurance. ~ Strong analytical, troubleshooting, and data validation skills with exceptional attention to detail. ~ Excellent communication skills and the ability to translate business requirements into technical solutions. ~ Bachelors degree in Computer Science, Data Analytics, Finance, or a related field, or equivalent practical experience. Experience building dashboards, reports, or data applications in Sigma Computing. Exposure to Power BI, Tableau, or other business intelligence platforms. Experience with Snowflake and cloud environments such as AWS. Familiarity with data orchestration and workflow tools such as Airflow. Exposure to dbt, DataHub, data lineage, or automated data quality testing. Experience within private equity, real estate, asset management, or financial services. Understanding of fund reporting, investment performance, or portfolio operations. Primary BI Platform: SQL: Joins, CTEs, aggregations, window functions, query optimization Data Warehouse: Data Modeling: Dimensional modeling, fact and dimension tables, star schemas Data Transformation: dbt or equivalent Cloud Infrastructure: AWS or similar cloud platforms Data Pipelines: Data Quality: Testing, validation, reconciliation, and troubleshooting Development Practices: Git, version control, documentation, and deployment workflows Secondary BI Tools: Power BI, Tableau, or similar platforms