Data Engineer [T500-29045]
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Key skills for this role
Role Overview
The Data Engineer will build and optimize data infrastructure supporting analytics and AI initiatives.
The role focuses on reliable pipelines, data quality, performance, and trusted data access for analysts, data scientists, and business stakeholders.
The engineering team uses modern tools and provides training, mentoring, and opportunities to shape technology practices.
Key Skills for This Role
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About Stolt-Nielsen
Stolt-Nielsen is a global provider of bulk-liquid and chemical logistics, transportation, and storage.
The Digital Innovation Centre builds systems and supports digital transformation across the logistics business.
About the Role
The Data Engineer will build and optimize data infrastructure supporting analytics and AI initiatives.
The role focuses on reliable pipelines, data quality, performance, and trusted data access for analysts, data scientists, and business stakeholders.
The engineering team uses modern tools and provides training, mentoring, and opportunities to shape technology practices.
Required Skills
- Required technologies include Databricks, Python, SQL, Lakehouse Medallion architecture, Unity Catalog, REST APIs, event-driven architectures, Azure data and storage services, Git, CI/CD, and infrastructure as code.
- The role also mentions Cursor or other AI-enabled IDEs and workflows.
What You’ll Do
- Design, develop, and maintain scalable Databricks data pipelines using PySpark and Delta Lake.
- Build ETL/ELT processes to ingest data from APIs, databases, files, and cloud systems.
- Optimize data models and storage for analytics, machine learning, and BI reporting.
- Write SQL queries, views, and stored procedures for transformation and analysis.
- Collaborate with cross-functional teams to translate business needs into technical solutions.
- Implement data quality, governance, and security standards across platforms.
- Monitor and troubleshoot workflows for performance, reliability, and cost efficiency.
- Stay current with data engineering and cloud-native data architecture practices.
What You’ll Bring
- A bachelor’s or master’s degree in computer science or a related field is required.
- At least 3 years of hands-on experience as a Data Engineer, ETL Developer, or similar is required.
- Strong Python skills, especially PySpark and pandas, and advanced SQL capability are required.
- Hands-on Databricks experience must include workspaces, notebooks, jobs, clusters, and Unity Catalog.
- Experience with data warehousing, data modeling, distributed processing, Azure PaaS, CI/CD, automated testing, and Agile delivery is required.
- Analytical ability, attention to detail, effective communication, and cross-functional collaboration are required.
Must-Have Skills
- Databricks with PySpark, Delta Lake, jobs, and workflows.
- Python and PySpark for data processing and automation.
- Advanced SQL for querying and performance tuning.
- Data pipeline orchestration, data modeling, Git, CI/CD, and Unity Catalog.
Nice-to-Have Skills
- Familiarity with dbt or modern data stack tools.
- Knowledge of governance, cataloging, and access control tools such as Unity Catalog or Purview.
- Exposure to machine learning workflows and model data preparation.
- Experience with Databricks performance tuning and cost optimization.
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