Lead Business Intelligence Analyst

Autodesk•San Francisco, CA

1d•$105,000 - $195,000•Hybrid

About The Position

This role focuses on producing financial and market intelligence by querying data repositories and generating reports. The analyst will devise methods for identifying data patterns and trends, work cross-functionally to meet stakeholder requirements, and translate business objectives into technical data requirements. Responsibilities include performing deep dive analyses, crafting data stories through presentations and visualizations, communicating findings, documenting data provenance and ETL logic, and spearheading the development of insight-driven tools and dashboards. Some telecommuting is permitted.

Requirements

Master’s degree in Business Analytics, Data Analytics /Engineering, Information Systems or related field
Three (3) years of experience in the job offered or in a business analyst-related occupation.
At least two (2) years of experience utilizing knowledge of Alteryx to automate data workflows, perform ETL processes, enabling efficient data preparation, transformation, and visualization for actionable insights.
At least two (2) years of experience developing and optimizing Power BI dashboards, performing validation testing, and implementing continuous improvements post-launch by collaborating with global teams to align with stakeholder requirements.
At least two (2) years of experience utilizing knowledge of Microsoft Excel to analyze trends, build reports, and transform raw data into actionable insights for data-driven solutions.
At least two (2) years of experience utilizing knowledge of team leadership and mentorship to guide associates, enhance their analytical skills, and improve project execution.
At least one semester of graduate-level coursework or some experience utilizing knowledge of Snowflake/Presto databases and querying tools such as Oracle to write and optimize queries relevant to answer business questions and deep dive analyses.
At least one semester of graduate-level coursework or some experience utilizing knowledge of Agile methodologies, Jira administration, and project management to drive enterprise Agile transformations, enhance project efficiency, and support technology migration efforts through auditing and seamless execution.
At least one semester of graduate-level coursework or some experience maintaining critical data models, datasets, and dashboards across the operational reporting ecosystem while streamlining processes to identify opportunities for standardization and simplification, shaping the Business Intelligence roadmap.
At least one semester of graduate-level coursework or some experience utilizing knowledge of data visualization and storytelling to craft data-driven narratives through presentations, demonstrations, and design reports.
At least one semester of graduate-level coursework or some experience utilizing knowledge of Python to clean and preprocess data, while leveraging statistical techniques—including sampling distributions, hypothesis testing, linear regression, and logistic regression—to extract insights, perform in-depth analyses, and deliver data-driven recommendations.

Responsibilities

Produce financial and market intelligence by querying data repositories and generating periodic reports.
Devise methods for identifying data patterns and trends in available information sources.
Work collaboratively and cross functionally to define and meet stakeholder requirements.
Translate business objectives into technical data requirements and balance them with technical feasibility, recommending changes in development, maintenance and platform standards as necessary.
Perform deep dive analyses to understand trends, anomalies and insights that will drive operational improvements.
Craft data stories through presentations, written summaries, and data visualizations that accurately outline problem statements and provide actionable and unbiased intelligence and recommendations.
Communicate findings from initiatives with clarity and accountability to the broader organization/stakeholders.
Clearly document provenance of data, ETL logic, and code used to develop models.
Proactively identify areas in which analytics efforts can answer business questions, drive operational improvements and business value.
Spearhead the development of insight-driven tools & dashboards and provide ongoing support regarding functionality and data-integrity.