Data Engineer

As a Data Engineer, you will be at the core of advancing and scaling our procurement data initiatives. Your primary focus will be to evolve and maintain our data lake — built on Google Cloud native technologies, transforming complex, raw procurement data into structured, insightful outputs that drive strategic and operational decision-making across the organization.

Key Responsibilities

Data Pipelines & Integration: Design, build, and adapt data pipelines to ingest structured and semi-structured data into the data lake, ensuring performance, lineage, and scalability.

Data Transformation: Transform raw data into analytics-ready formats using SQL, Python, and ETL frameworks such as DBT and Vertex AI pipelines.

Data Modeling: Design and implement efficient and maintainable data models to optimize query performance and enable self-service analytics.

Data Quality & Security: Implement robust error handling, anomaly detection, and quality checks to ensure data integrity.

CI/CD & Workflow Automation: Develop and manage CI/CD pipelines to ensure smooth deployment of data workflows.

Data Governance: Collaborate with tech teams to develop and maintain governance frameworks for procurement data, including metadata management, cataloging, and access provisioning.

Data Products: Partner with business stakeholders to develop data products that support both internal operations and client-enabled services.

Skills & Requirements

• Bachelor's degree in business analytics, data science, computer science or a related field.
• Experience with cloud-based data platforms (GCP) and ETL tools (DBT, Vertex AI, Cloud Composer, Apache Airflow or other orchestration tools).
• Knowledge of agile product management methodologies and tools (Jira).
• Strong proficiency with SQL, Python, R, or akin platforms. Knowledge of data visualization tools is beneficial (i.e. Power BI).
• Experience in designing and maintaining automated data pipelines using tools such as Airflow, Cloud Composer, or other workflow orchestration systems.
• Strong analytical and problem-solving approach, being able to use data and insights to drive informed decision-making, as well as identifying process improvements to drive operational efficiency.
• Familiarity with data security, privacy regulations (GDPR, CCPA), and governance practices in the context of large-scale data platforms.