Full-time
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Job description
Head count : 1 at:
- NYC (primary) New York City, NY 10001
Project duration : 10 weeks
Work from Office : 5 days in a week
Key Responsibilities:
1. Data Hygiene and Quality Assurance
• Assess, profile, and cleanse sales and revenue-related data from various sources (CRM, ERP, marketing automation, etc.).
• Establish, monitor, and enforce data quality rules to ensure accuracy, consistency, and completeness of critical datasets.
• Work with business partners to drive upstream improvements in data entry and management.
2. Reporting & Analytics Foundation
• Design, build, and automate standardized reporting datasets and dashboards to measure sales performance, pipeline health, forecasting, and revenue trends.
• Lay out and document the architecture for data models and reporting layers; ensure scalability and flexibility for evolving business needs.
• Identify gaps in data flows or reporting logic and proactively resolve root causes.
3. Data Integration & Lineage
• Own extraction, transformation, and loading (ETL) of data from multiple systems into a central reporting environment (data warehouse/data lake).
• Develop and maintain data lineage documentation to track where data originates, how it flows, and how it is transformed through each stage.
• Partner with IT and Sales Ops teams to ensure seamless, well-governed data pipelines.
4. Data Conformance & Governance
• Define and implement business rules to conform disparate data into unified views (e.g., standardizing customer/account IDs, sales stages, product hierarchies).
• Collaborate on establishing data governance, stewardship roles, and best practices for the Sales organization.
• Drive adoption of data standards and conformance practices across the Sales and RevOps teams.
5. Stakeholder Engagement & Enablement
• Translate reporting requirements from Sales, RevOps, and Marketing teams into functional data assets.
• Provide data expertise and consulting to internal partners, delivering ad-hoc analyses and strategic insights.
• Train and empower business users on self-service analytics tools.
Qualifications and Skills:
- Bachelor's or Master's degree in Data Science, Information Systems, Business Analytics, or a related field.
- experience in data analysis, preferably supporting Sales, Revenue Operations, or related functions.
- Strong expertise in data cleansing, integration, and reporting with tools such as SQL, Power BI, Tableau, or equivalent.
- Deep understanding of B2B sales processes and common CRM/sales data management practices.
- Demonstrated ability to document and communicate data lineage, business logic, and governance controls.
- High attention to detail, proactive approach to solving data challenges, and strong cross-functional communication skills.
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Frequently asked questions
Q: What skills or qualities help someone succeed as a Data Analyst?
A: To succeed as a Data Analyst, key technical skills include proficiency in programming languages such as Python or R, expertise in data visualization tools like Tableau or Power BI, and knowledge of statistical analysis and machine learning concepts. Additionally, strong soft skills like effective communication, problem-solving, and collaboration are crucial for presenting insights to stakeholders and working with cross-functional teams. By combining these technical and soft skills, Data Analysts can drive business decisions, identify areas for improvement, and contribute to the growth and success of their organization.
Q: What is the career path for a Data Analyst?
A: A Data Analyst's typical career progression involves starting as an Entry-Level Data Analyst, where they collect, analyze, and interpret data to inform business decisions. As they gain experience, they can move into Mid-Level roles such as Senior Data Analyst or Business Analyst, where they take on more complex projects and lead smaller teams. Ultimately, they can advance to Senior Leadership positions like Data Scientist, Data Manager, or even Director of Analytics, where they oversee large-scale data initiatives and drive strategic business growth.\n\nKey opportunities for skill development and professional growth in this role include learning programming languages like Python or R, mastering data visualization tools like Tableau or Power BI, and staying up-to-date with emerging trends in machine learning and artificial intelligence. Additionally, Data Analysts can develop soft skills like communication, project management, and leadership to excel in their roles.\n\nLong-term career prospects for Data Analysts are diverse, with potential directions including transitioning into related fields like Business Intelligence, Data Engineering, or even becoming a Product Manager, or pursuing advanced degrees in Data Science or related fields to further specialize in areas like machine learning or data engineering.