Sand Technologies is a fast-growing enterprise AI company that solves real-world problems for large blue-chip companies and governments worldwide.
We’re pioneers of meaningful AI: our solutions go far beyond chatbots. We are using data and AI to solve the world’s biggest issues in telecommunications, sustainable water management, energy, healthcare, climate change, smart cities, and other areas that have a real impact on the world.
ABOUT THE ROLE
Sand Technologies focuses on cutting-edge cloud-based data projects, leveraging tools such as Databricks, DBT, Docker, Python, SQL, and PySpark. We work across a variety of data architectures such as Data Mesh, lakehouse, data vault, and data warehouses. Our data engineers create pipelines that support our data scientists and power our front-end applications.
JOB SUMMARY
A Data Engineer has the primary role of designing, building, and maintaining scalable data pipelines and infrastructure to support data-intensive applications and analytics solutions. They closely collaborate with data scientists, analysts, and software engineers to ensure efficient data processing, storage, and retrieval for business insights and decision-making.
RESPONSIBILITIES
1. Data Pipeline Development: Design, implement, and maintain scalable data pipelines for ingesting, processing, and transforming large volumes of data from various sources.
2. Data Modeling: Design and optimize data models and schemas for efficient storage, retrieval, and analysis of structured and unstructured data.
3. ETL Processes: Develop and automate ETL workflows to extract data from diverse sources, transform it into usable formats, and load it into data warehouses, data lakes, or lakehouses.
4. Big Data Technologies: Utilize big data technologies for distributed data processing and analytics.
5. Cloud Platforms: Deploy and manage data solutions on cloud platforms, leveraging cloud-native services for data storage, processing, and analytics.
6. Data Quality and Governance: Implement data quality checks, validation processes, and data governance policies to ensure accuracy, consistency, and compliance with regulations.
7. Monitoring, Optimization, and Troubleshooting: Monitor data pipelines and infrastructure performance, identify bottlenecks and optimize for scalability, reliability, and cost-efficiency.
8. Collaboration: Collaborate with cross-functional teams to understand requirements, define data architectures, and deliver data-driven solutions.
9. Documentation: Create and maintain technical documentation to facilitate understanding and maintainability of data solutions.
10. Best Practices: Continuously learn and apply best practices in data engineering and cloud computing.
QUALIFICATIONS
1. Proven experience as a Data Engineer, or in a similar role, with hands-on experience building and optimizing data pipelines and infrastructure.
2. Strong problem-solving and analytical skills with the ability to diagnose and resolve complex data-related issues.
3. Solid understanding of data engineering principles and practices.
4. Excellent communication and collaboration skills to work effectively in cross-functional teams.
5. Ability to write clean, scalable, robust code using Python or similar programming languages.
DESIRABLE LANGUAGES/TOOLS
1. Proficiency in programming languages such as Python, Java, Scala, or SQL for data manipulation and scripting.
2. Experience in big data technologies and frameworks.
3. Experience with CI/CD pipelines and version control systems like Git.
4. Familiarity with cloud platforms and services for deploying and managing data solutions.
Would you like to join us as we work hard, have fun and make history?
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