Data Analyst responsible for strategic analysis and process improvement at Ipiranga. Focus on data management, KPI monitoring, and complex problem - solving within a hybrid workplace.
About the role
- Commercial Data Intelligence Analyst delivering data strategy and advancing analytics for AVEVA. Collaborating with teams to design ETL pipelines and support AI initiatives while ensuring data quality and usability.
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.
Requirements
- Bachelor’s 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.
Benefits
- Gratuity
- Medical and accidental insurance
- very attractive leave entitlement
- emergency leave days
- childcare support
- maternity, paternity and adoption leaves
- education assistance program
- home office set up support (for hybrid roles)
- well-being support
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
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