Responsibilities
- Lead full-cycle analytics projects, guiding them from initial stakeholder input through deployment, verification, automation, and organizational uptake.
- Work closely with business, analytics, data, and technical teams to establish goals, define performance indicators, and transform unclear requests into structured analytics tasks.
- Create, refine, and validate complex SQL queries across large enterprise data environments.
- Build robust, scalable analytics systems that enable operational decisions, product evaluation, root-cause investigations, and routine business reporting.
- Integrate artificial intelligence and automation into BI processes, such as natural language query translation, AI-assisted reporting, automated insight detection, anomaly investigation, and data integrity monitoring.
- Develop, assess, test, and improve AI-enhanced analytics workflows to ensure they are accurate, interpretable, repeatable, and trusted by business users.
- Find ways to minimize manual reporting, simplify stakeholder inquiries, boost analytical efficiency, and enhance overall BI team output.
- Ensure reliability of analytics results through thorough data validation, metric alignment, source verification, and stakeholder review.
- Coordinate with BI developers, data engineers, product analysts, and business partners to standardize data models, metric definitions, and reporting logic.
- Produce detailed documentation covering analytical logic, SQL assumptions, data provenance, AI workflow behavior, known constraints, and outputs for stakeholders.
- Function autonomously in a dynamic environment with minimal onboarding, proactively identifying risks, dependencies, obstacles, and required decisions.
- Clearly convey insights, trade-offs, risks, and recommendations to both technical and non-technical audiences.
- Drive adoption of analytics and AI-powered BI tools by training users, collecting feedback, tracking usage, and refining workflows based on input.
- Contribute to establishing reusable frameworks for agentic BI delivery, including evaluation criteria, validation procedures, governance standards, and automation guides.
- Provide mentorship to analysts or engineers in advanced SQL techniques, AI-assisted analytics, business problem framing, and ownership of end-to-end projects.
Compensation
Competitive salary and benefits package
Work Arrangement
Flexible work environment with remote options
Team
Part of a high-performing analytics and business intelligence team driving data-led decisions
Responsibilities (15)
- Lead full-cycle analytics projects, guiding them from initial stakeholder input through deployment, verification, automation, and organizational uptake.
- Work closely with business, analytics, data, and technical teams to establish goals, define performance indicators, and transform unclear requests into structured analytics tasks.
- Create, refine, and validate complex SQL queries across large enterprise data environments.
- Build robust, scalable analytics systems that enable operational decisions, product evaluation, root-cause investigations, and routine business reporting.
- Integrate artificial intelligence and automation into BI processes, such as natural language query translation, AI-assisted reporting, automated insight detection, anomaly investigation, and data integrity monitoring.
- Develop, assess, test, and improve AI-enhanced analytics workflows to ensure they are accurate, interpretable, repeatable, and trusted by business users.
- Find ways to minimize manual reporting, simplify stakeholder inquiries, boost analytical efficiency, and enhance overall BI team output.
- Ensure reliability of analytics results through thorough data validation, metric alignment, source verification, and stakeholder review.
- Coordinate with BI developers, data engineers, product analysts, and business partners to standardize data models, metric definitions, and reporting logic.
- Produce detailed documentation covering analytical logic, SQL assumptions, data provenance, AI workflow behavior, known constraints, and outputs for stakeholders.
- Function autonomously in a dynamic environment with minimal onboarding, proactively identifying risks, dependencies, obstacles, and required decisions.
- Clearly convey insights, trade-offs, risks, and recommendations to both technical and non-technical audiences.
- Drive adoption of analytics and AI-powered BI tools by training users, collecting feedback, tracking usage, and refining workflows based on input.
- Contribute to establishing reusable frameworks for agentic BI delivery, including evaluation criteria, validation procedures, governance standards, and automation guides.
- Provide mentorship to analysts or engineers in advanced SQL techniques, AI-assisted analytics, business problem framing, and ownership of end-to-end projects.
Available for qualified candidates
