Senior Business Data Analyst

PCOnline; Newark (DE); On-site; USD 80,000 - 110,000; Full time

Job summary

PCOnline is seeking a Senior Business Data Analyst to improve data quality and reporting efficiency. The ideal candidate will work closely with cross-functional teams to define metrics, automate reporting, and deliver actionable insights for data-driven decision-making.

Applicants should hold a Master's degree with minimum 3 years of experience and must demonstrate strong SQL skills and proficiency in data analysis tools. Familiarity with BI platforms is also essential for success in this role.

Qualifications

• 3+ years of experience in business data analysis or related field.
• Proficiency in standard data analytics tools and SQL.
• Experience with BI platforms and dashboard creation.

Responsibilities

• Collaborate with teams on business objectives and data pain points.
• Conduct data analysis and recommend actionable insights.
• Create and maintain metric documentation and data governance.

Skills

Strong SQL skills

Data analysis tools proficiency (Python, Excel)

Stakeholder management

Business understanding

Education

Master’s degree in Business Analytics or related field

Tools

Power BI

Tableau

SQL databases

Job description

As a Senior Business Data Analyst, you will play a key role in improving the company’s data quality, reporting efficiency, business visibility, and data-driven decision-making capabilities. This role goes beyond creating dashboards or supporting ad-hoc data requests. You will work closely with cross-functional teams, including Finance, Product Management, IT, Digital Marketing, Marketplace Operations, Warehouse Operations, and other business functions, to define standardized business metrics, improve data consistency, automate recurring reporting processes, and deliver actionable insights that support operational efficiency and business growth. The ideal candidate should have strong business understanding, hands-on data analysis skills, and the ability to translate complex business processes into clear data logic, reporting requirements, and scalable data solutions. You should be comfortable working with multiple systems, identifying data quality issues, aligning metric definitions across teams, and building reliable dashboards, reports, and automated workflows. This role requires strong ownership, attention to detail, and the ability to work independently with both business stakeholders and technical teams.

Key Responsibilities

• Work closely with cross-functional teams to understand business objectives, operational processes, reporting needs, data pain points, and decision-making scenarios across areas such as marketplace operations, digital marketing, warehouse operations, supply chain, finance, product management, and internal systems.
• Conduct data analysis to identify business trends, operational inefficiencies, abnormal patterns, data discrepancies, and improvement opportunities, then communicate findings clearly to business owners with practical and actionable recommendations.
• Define, document, and standardize key business metrics, including business logic, calculation rules, data sources, refresh frequency, reporting ownership, and usage scenarios, ensuring that different departments use consistent and reliable data definitions.
• Create and maintain data dictionaries, metric documentation, reporting logic, and business data requirement documents to support long-term data governance, reduce reporting ambiguity, and improve cross-functional communication.
• Support data quality management by defining validation rules, monitoring data anomalies, investigating root causes, and coordinating with relevant teams to resolve data issues and prevent repeated reporting errors.
• Support analytical modeling when appropriate, including forecasting, segmentation, trend analysis, performance tracking, and operational optimization, while ensuring that analytical outputs are explainable, practical, and business-relevant.

Basic Qualifications

• Master’s degree in Business Analytics, Data Science, Computer Science, Statistics, Mathematics, Information Systems, Operations Research, or a related quantitative field.
• 3+ years of experience in business data analysis, business analytics, data analytics, reporting automation, data governance, operations analytics, marketplace analytics, supply chain analytics, finance analytics, or a related field.
• Strong hands-on SQL skills are required, including data extraction, joins, aggregations, data validation, business logic implementation, and analysis across multiple tables or systems.
• Proficiency in at least one data analysis or automation tool, such as Python, advanced Excel, Power Query, VBA, or similar tools. Python experience for data processing, automation, or analysis is strongly preferred.
• Experience building and maintaining dashboards, reports, or business performance tracking tools using BI platforms such as Superset, Tableau, Power BI, Looker, Metabase, or similar tools.
• Strong understanding of business metric definition, data quality validation, reporting logic, data dictionaries, metric documentation, and data consistency across business teams.
• Ability to understand complex business processes and translate them into clear data logic, reporting requirements, metric definitions, and process improvement recommendations.
• Strong communication and stakeholder management skills, with the ability to explain data logic, analysis findings, reporting limitations, and business implications clearly to non-technical stakeholders.
• Experience in retail, e-commerce, marketplace operations, digital marketing analytics, supply chain, finance operations, or similar operational environments is highly preferred.
• Experience working with ERP, WMS, OMS, marketplace platforms, inventory systems, order systems, finance systems, or other operational business systems is a strong plus.
• Experience with data warehouse concepts, ETL/ELT processes, data modeling, data lineage, or collaboration with data engineering/product/IT teams is preferred.
• Knowledge of statistical modeling techniques such as regression, clustering, forecasting, time series analysis, or anomaly detection is a plus, but this role prioritizes business data ownership, data quality, metric standardization, reporting automation, and operational impact over pure machine learning research.