Sr Advanced Data Analyst

Role Overview

As a Sr Advanced Data Analyst supporting Honeywell Building Automation, you will play a critical role in transforming global project and account data into actionable commercial intelligence for sales teams, sales leaders, and business executives.

This is not a traditional IT analyst or dashboard-reporting role. The successful candidate will sit at the intersection of data quality, business analysis, sales operations, market intelligence, and CRM execution. The role requires someone who can look beyond the data fields themselves and determine whether the information is accurate, commercially useful, complete, and ready for sales consumption.

The individual will support Honeywell Building Automation’s demand generation and project intelligence efforts by validating project data, enriching missing or incorrect information, identifying business-relevant signals, and helping prioritize and route project leads to the appropriate sales managers and sellers. This person will work closely with business stakeholders, BI developers, data engineers, sales operations, and commercial leaders to ensure that project intelligence is not only technically available, but also trusted, relevant, and actionable.

The ideal candidate is analytically strong, commercially curious, detail-oriented, and comfortable operating in ambiguity. They should be able to move between granular data validation, business-rule design, CRM logic, sales feedback, and leadership-facing analysis.

Purpose of the Role

Honeywell Building Automation is building a more scalable and disciplined approach to identifying, enriching, prioritizing, and routing global project opportunities to sales teams. This requires more than data extraction or dashboard development. It requires a business-minded analyst who can determine whether a project record is meaningful, whether the owner, engineer, contractor, vertical, construction timing, and opportunity signals are accurate, and whether the lead should be prioritized for action.

This role will help ensure that project intelligence is converted into sales-ready leads, with the right level of data quality, business context, prioritization logic, and routing discipline.

Key Responsibilities

1. Data Validation and Business-Ready Data Quality

• Validate global project and account data from a business and sales-consumption perspective, not only from a technical completeness perspective.
• Review project records to determine whether the information is accurate, current, commercially relevant, and usable by sales teams.
• Identify and correct inaccurate, outdated, duplicated, incomplete, or misleading project data across fields such as owner, developer, end user, engineer, architect, general contractor, vertical, project status, construction timing, CAPEX, location, and opportunity relevance.
• Use internal sources, paid data services, public internet research, CRM data, and sales feedback to validate, enrich, replace, or supplement project information.
• Assess whether data fields are logically consistent. For example, whether a construction start date aligns with the project status, whether the listed contractor is current or historical, whether the owner is the true decision-maker, or whether the project is still actionable for Honeywell.
• Develop practical data-quality rules that reflect how sales teams actually consume and act on project leads.

1. Project Lead Enrichment and Commercial Intelligence

• Enrich project leads with relevant commercial context, including building owner, developer, engineer, architect, contractor, vertical, project stage, strategic account linkage, installed base relevance, and Honeywell opportunity fit.
• Research missing or unclear data points using paid data platforms, public sources, company websites, construction databases, press releases, public tender portals, CRM history, and other reliable sources.
• Distinguish between data that is technically present and data that is commercially useful.
• Identify sales-relevant insights that help determine whether a project should be pursued, validated, deprioritized, routed to a specific sales team, or flagged for further review.
• Support the development of seller-ready lead views that help sales managers understand why a project matters, what is known, what is missing, and what action should be taken next.

1. Lead Prioritization and Routing Logic

• Help formulate, test, and continuously improve the business logic used to prioritize and route project leads to Building Automation sales managers and sellers.
• Define business rules that determine how leads should be assigned based on country, region, vertical, account ownership, strategic account linkage, installed base, product fit, project size, project timing, project stage, and sales coverage model.
• Partner with BI developers and data engineers who will automate the routing logic, while owning the business rationale, rule definition, exception handling, and continuous improvement loop.
• Monitor whether routing outputs are accurate and commercially sensible, and identify where logic needs to be refined.
• Use feedback from sales managers and sellers to improve lead quality, prioritization rules, routing decisions, and actionability.
• Identify edge cases, duplicate ownership scenarios, strategic account conflicts, and routing exceptions that require business judgment.

1. CRM, Master Data, and Sales Process Alignment

• Support alignment between project intelligence, CRM records, master data, account hierarchies, and sales ownership structures.
• Work with CRM and sales operations teams to understand how project leads should connect to accounts, opportunities, sales territories, and ownership rules.
• Help identify where account names, building owners, subsidiaries, sub-brands, alternate names, or end users need to be linked more effectively to strategic accounts or existing customer records.
• Support the creation and improvement of structured datasets that can be loaded into Salesforce, Power BI, or other commercial systems by the appropriate technical teams.
• Understand the downstream implications of poor data quality on seller adoption, sales execution, pipeline creation, and leadership reporting.

1. Analysis, Reporting, and Leadership Support

• Support leadership-facing analyses related to project pipeline, data quality, lead readiness, sales adoption, routing effectiveness, market coverage, and demand generation.
• Help translate complex data findings into clear business messages, PowerPoint slides, executive summaries, and action-oriented recommendations.
• Create or support analyses that answer practical business questions, such as which project leads are most ready for seller action, where data quality gaps are concentrated, which regions or verticals have the strongest project signals, and where routing logic is underperforming.
• Partner with business leaders to shape analyses that are clear, concise, and suitable for senior leadership consumption.
• Support preparation of materials for operating reviews, sales leadership discussions, regional reviews, project pipeline reviews, and executive updates.

1. Continuous Improvement and Governance

• Help establish repeatable processes for project data validation, enrichment, prioritization, routing, and sales feedback capture.
• Identify recurring data issues and recommend process, source, or logic improvements to reduce manual rework over time.
• Support the development of governance standards for what qualifies as a sales-ready project lead.
• Track and communicate gaps in data quality, source reliability, logic performance, and adoption by sales teams.
• Continuously improve how project intelligence is transformed into actionable demand-generation support.

How This Role Is Different from a Typical Data Analyst Role

A typical data analyst may focus primarily on extracting data, creating reports, building dashboards, or summarizing trends. This role requires a broader and more business-critical skillset.

This person must be able to determine whether data is commercially accurate, whether it makes sense in the real world, whether it is useful for a sales manager, and whether it should trigger action.

This role requires judgment, not just analysis. The candidate must be able to ask questions such as:

• Is this actually the right building owner or just a related entity?
• Is this project still active, or is the data outdated?
• Is the general contractor appointed, historical, or unknown?
• Is the project early enough for Honeywell to influence the specification?
• Does this project map to a strategic account, existing customer, partner, or installed base?
• Should this lead be routed to a regional sales manager, strategic account owner, channel seller, direct seller, or held for validation?
• Is the data strong enough for sales follow-up, or does it need further enrichment?

The right candidate will not simply report what the data says. They will challenge it, improve it, contextualize it, and help turn it into action.

Candidate Profile

The ideal candidate combines strong analytical skills with business judgment, curiosity, and commercial orientation.

They should be comfortable working with imperfect data and should enjoy the process of investigating, validating, and improving information. They should be able to work across structured datasets, CRM records, paid data sources, public research, and stakeholder input to produce a more reliable and actionable view of the market.

This role is well suited for someone who has experience in data analysis, business analysis, sales operations, commercial operations, market intelligence, master data management, or CRM analytics, and who wants to play a more strategic role in connecting data to commercial execution.

The candidate should be able to operate independently, communicate clearly, and engage confidently with both technical teams and business stakeholders.

You Must Have

• Experience in data analysis, business analysis, commercial operations, sales operations, market intelligence, master data management, CRM analytics, or a related field.
• Strong ability to validate, clean, enrich, and interpret business data from multiple sources.
• Ability to assess data quality from a business-consumption perspective, not only from a technical or system-completeness perspective.
• Experience working with large datasets, structured data, and recurring data-quality issues.
• Strong analytical skills with the ability to identify patterns, inconsistencies, gaps, duplicates, and logic errors.
• Ability to conduct research using paid data sources, internet research, company websites, public sources, CRM data, and stakeholder feedback.
• Ability to translate business requirements into data rules, routing logic, prioritization frameworks, and repeatable processes.
• Strong communication skills, including the ability to summarize findings clearly for sales teams, managers, and senior leaders.
• Strong proficiency in Microsoft Excel and PowerPoint.
• Working knowledge of CRM systems, preferably Salesforce or similar platforms.
• Ability to work cross-functionally with BI developers, data engineers, sales operations, sales managers, and commercial leaders.
• Comfort operating in ambiguity and improving processes that are still being built or scaled.

We Value

• Experience in a B2B commercial environment, especially in sales operations, demand generation, market intelligence, project intelligence, account management, or pipeline development.
• Experience supporting sales teams with lead qualification, account matching, opportunity routing, territory logic, or CRM adoption.
• Experience with Salesforce, SAP, Power BI, Tableau, SQL, Python, or similar tools.
• Experience with master data management, account hierarchy mapping, entity resolution, duplicate detection, or data governance.
• Experience using external data sources, paid databases, construction intelligence platforms, tender portals, company registries, or public research to enrich business records.
• Ability to understand building projects, construction stages, owner/developer structures, engineering firms, contractors, and project decision-making ecosystems.
• Ability to create leadership-ready PowerPoint materials and translate analysis into executive-level storylines.
• Strong business judgment and willingness to challenge data when it does not make sense.
• High attention to detail and strong ownership mindset.
• Ability to balance speed, accuracy, and practical business usefulness.
• Ability to partner effectively with technical teams while representing the business logic and sales-consumption requirements.

Key Success Measures

• Improved quality, completeness, and reliability of project lead data.
• Higher confidence from sales teams in the project intelligence being provided.
• More accurate and actionable lead routing to sales managers and sellers.
• Clearer prioritization of project leads based on business value, timing, account relevance, and opportunity fit.
• Reduced volume of misleading, duplicated, outdated, or low-value project records reaching sales teams.
• Improved feedback loops between sales teams, data teams, and business stakeholders.
• Stronger leadership visibility into project pipeline quality, data gaps, and demand-generation opportunities.

Candidate Fit

This role may be a strong fit for candidates who are excited by:

• Turning messy, incomplete, or unreliable data into trusted commercial intelligence.
• Working at the intersection of data, sales, CRM, and business strategy.
• Investigating project and account information to determine what is true, what is missing, and what matters.
• Building logic that helps route the right opportunities to the right sales teams.
• Helping business leaders make better decisions using improved data and sharper analysis.
• Creating practical outputs that sales teams can actually use.

This role may not be the right fit for candidates who are primarily looking for:

• A pure IT analyst role.
• A back-office reporting role with limited business interaction.
• A role focused only on dashboards, automation, or technical data modeling.
• A role where requirements are always fully defined upfront.
• A role that does not require external research, business judgment, or direct engagement with commercial stakeholders.
• A role where data quality is treated only as a system issue rather than a business-actionability issue.

Working Relationships

This role will work closely with:

• Building Automation commercial leadership.
• Regional and country sales managers.
• Sales operations and CRM teams.
• BI developers and data engineers.
• Strategic account and channel teams.
• Market intelligence and demand-generation stakeholders.
• Business leaders consuming project pipeline and sales-readiness insights.

Required Mindset

The successful candidate must have a strong ownership mindset and the ability to think like a business partner, not only a data analyst. They should be willing to challenge assumptions, identify where data is misleading, and take responsibility for improving the quality and usefulness of the information being delivered to sales teams.

The role requires curiosity, precision, judgment, and commercial common sense. The objective is not simply to produce more data. The objective is to help Honeywell Building Automation convert better project intelligence into better sales action.