Commercial Data Intelligence Analyst

Key Responsibilities

• Commercial Data Intelligence Deliver and advance the commercial data strategy to ensure a single version of the truth for reporting, analytics, and AI/ML initiatives. Contribute to data warehouse and data lake initiatives to enable advanced analytics and AI-ready datasets.
• Data Engineering Pipelines Design and maintain ETL/ELT pipelines using Microsoft-based tools (e.g., Data Factory, Synapse, Fabric pipelines), ensuring data quality, integrity, and timeliness. Drive adoption of standardized data infrastructure by replacing non-standard data sources (Excel, manual files, SharePoint) with governed pipelines and curated datasets.
• AI-Ready Data Preparation Structure, cleanse, and enrich data to make it suitable for machine learning models, predictive analytics, and recommendation engines. Collaborate with data science teams to enable feature engineering and model deployment.
• Data Analysis Perform deep-dive and ad-hoc analyses, generating actionable insights to address commercial challenges and opportunities, with a focus on improving data pipelines and usability.
• Stakeholder Engagement Collaborate with senior commercial, sales, and operations leaders to understand requirements, design analytical solutions, and ensure data outputs meet business needs.
• SQL & Data Modelling Develop, optimize, and maintain complex SQL queries and data models to support scalable analytics and AI workflows.
• Continuous Improvement Identify and implement opportunities to improve data workflows, streamline manual processes, and enhance the reliability of commercial datasets. Stay current with Microsoft Fabric and cloud data engineering best practices.

Skills & Qualifications

• Bachelors degree in data engineering, Computer Science, Information Systems, or a related field.
• 3-5 years of experience in data integration, data engineering, or commercial data management roles.
• Strong SQL skills for data extraction, transformation, and validation across large datasets.
• Experience with cloud-based data platforms (Azure Synapse, Microsoft Fabric, Databricks, or similar).
• Knowledge of data modeling concepts (star schema, normalization / denormalization) and ETL best practices.
• Familiarity with Salesforce data structures and other commercial systems is a plus.
• Ability to design AI/ML-ready datasets and support analytics teams in feature engineering.
• Strong problem-solving skills with attention to detail and commitment to data quality.
• Excellent communication and collaboration skills to work with cross-functional stakeholders.