JI
Director of Operations to enhance JLL's Government, Education & Non - Profit Advisory group. Driving growth, margin, and client performance within the Public Institutions division.
About the role
- Revenue Operations Engineer optimizing Smartly’s platform infrastructure to drive revenue growth. Collaborating with cross-functional teams to enhance revenue tech stack and support data-driven decisions.
Responsibilities
- Collaborate with data engineering and data analytics to build and manage data pipelines to integrate revenue data from HubSpot, Zuora, and other business systems:
- Drive the creation of processes to transform and structure revenue data for analytics and reporting.
- Implement data cleansing, normalization, and enrichment strategies to ensure revenue data quality and governance.
- Automate revenue reporting and forecasting by integrating data sources into a unified analytics framework
- Support revenue analytics initiatives by designing data models for pipeline, customer lifecycle, and revenue trends.
- Collaborate with platform owners, e.g. HubSpot and Zuora, to implement processes and workflows that deliver productivity and efficiency gains for Smartly:
- Leverage data to develop workflow automation within HubSpot and Zuora to streamline lead -to -revenue processes across relevant revenue generating business functions, e.g. reduce and or automate handoffs between teams, identify signals for churn reduction.
- Implement automation to improve data entry, validation, and workflow consistency.
- Ensure that the revenue tech stack integrations (HubSpot, Zuora, and other revenue and analytics platforms) are synchronized and optimized for accuracy
- Collaborate with platform owners to improve system integrations and workflow efficiency.
- Collaborate with the Product and Engineering team to implement API -driven integrations between revenue platforms and Smartly’s product ecosystem.
- Collaborate with data engineers and data scientists to enable real -time revenue analytics and dashboards, enabling business teams to track pipeline health, conversion rates, and revenue KPIs.
- Implement A/B testing for process changes, measuring impact on revenue efficiency and deal velocity.
- Monitor revenue operations system performance, data synchronization issues, and automation failures, troubleshooting as needed.
- Work closely with data and business teams to align on revenue data solutions needs.
- Collaborate with Sales, Marketing, Customer Success and Finance to improve revenue data flows, automation, and reporting accuracy.
- Act as a bridge between technical and business stakeholders, translating revenue operations needs into scalable system solutions.
- Carry out other duties as required from time to time
Requirements
- Bachelor’s degree in a relevant field such as Data, Engineering, Statistics, or a related quantitative field. Demonstrable track record delivering complex data engineering and analytics projects successfully:
- Technical Expertise: Proficient in Python and SQL for data handling; advanced Excel skills (e.g., formulas, pivot tables, macros). Working knowledge of cloud platforms.
- Data Management: Experience in data cleaning, transformation, validation, and reporting.
- Customer Tech Stack/Revenue Operations Platforms: CRM & Billing Systems knowledge is a bonus.
- System Integrations & APIs: Familiarity integrating revenue platforms using API calls, middleware, or no-code automation tools (e.g., n8n, Zapier) is also a bonus.
- Data Visualization: Experience in producing data visualization solutions using tools like Power BI or similar platforms. Strong data presentation, visualization and report writing skills.
- Communication Skills: Strong ability to explain complex data processes to nontechnical stakeholders.
- Problem-Solving: Aptitude for identifying and resolving data-related issues efficiently.
- Attention to Detail: Meticulous in ensuring data accuracy and consistency.
- Strong customer focus, emotional intelligence and business acumen.
- Experience and proficiency sourcing, validating and documenting requirements.
- Data engineering expertise: Solid understanding of data engineering fundamentals and concepts. Familiarity with orchestration, ingestion, and transformation of multisource datasets.
- Data modelling: Solid understanding of data modelling for analytics purposes.
- Collaboration and teamwork: You'll thrive working a wide community of business and technical stakeholders to drive projects forward. Comfortable working independently as well as closely collaborating across functions to identify problems and deliver high quality solutions with business and technical rigor
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