Job Description
– Proficient Data Engineer
Seniority: Proficient
Language: Advanced English required
About the Role
We are looking for a Proficient Data Engineer to join a cross-functional team focused on building scalable Data & Analytics solutions for business users across the organization. This role is critical to enabling trusted, high-quality, and well-structured data that powers dashboards, analytics, and future AI-driven use cases.
The ideal candidate will be responsible for building and maintaining robust data foundations—from raw data ingestion to transformation, semantic modeling, and delivery—ensuring that data products are reliable, reusable, and production-ready. You will work closely with data architects, analytics engineers, and BI developers to design efficient pipelines, improve data quality, and support scalable reporting and analytics capabilities.
This is a hands-on role for someone who enjoys solving data challenges, building modern pipelines, and applying engineering best practices in cloud-based data platforms.
Key Responsibilities
1. Data Pipeline Development
- Design, build, and maintain scalable, reliable ETL/ELT pipelines using Snowflake, AWS Glue, dbt, SQL, and Jinja-based transformations
- Develop and manage ingestion workflows from multiple data sources, supporting both batch and near-real-time data processing.
- Implement best practices for orchestration, modular pipeline design, error handling, and reusability.
- Optimize data workflows for performance, scalability, and cost efficiency in cloud environments.
2. Data Modeling & Transformation
- Build clean, maintainable, and reusable transformation layers using dbt and SQL
- Create well-structured data models that support business intelligence, analytics, and downstream applications.
- Apply semantic and dimensional modeling practices to ensure data is easy to understand and consume.
- Use Jinja to parameterize and standardize transformations across data pipelines.
3. Data Quality & Reliability
- Define and implement data quality checks, validation rules, and monitoring processes to ensure production-grade data reliability.
- Troubleshoot and resolve pipeline failures, data inconsistencies, and performance bottlenecks.
- Ensure data lineage, traceability, and consistency across ingestion and transformation layers.
- Contribute to data governance practices by documenting schemas, transformations, and ownership.
4. Collaboration & Engineering Best Practices
- Work closely with data architects, BI engineers, analysts, and cross-functional teams to deliver high-quality data solutions.
- Collaborate through GitHub using version control, pull requests, code reviews, and CI/CD best practices.
- Promote engineering standards for documentation, testing, deployment, and pipeline maintainability.
- Support knowledge sharing and contribute to continuous improvement across the data engineering practice.
5. Enablement for Analytics & AI
- Deliver trusted datasets and reusable data assets that support dashboards, business reporting, and advanced analytics initiatives.
- Help prepare the data ecosystem for future AI/ML and LLM-based use cases by ensuring data is structured, discoverable, and reusable.
- Stay current on modern data engineering trends, tools, and best practices, recommending improvements where appropriate.
Required Qualifications
- Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field, or equivalent practical experience.
- Proven experience as a Data Engineer working with modern cloud-based data platforms.
- Strong hands-on experience with:
- Python
- Snowflake
- AWS Glue
- SQL
- dbt
- Jinja
- GitHub
- Data pipeline development and orchestration
- Solid understanding of ETL/ELT architecture, data modeling, and transformation best practices.
- Experience designing scalable, maintainable, and testable data solutions.
- Strong analytical and problem-solving skills.
- Advanced English communication skills, both written and verbal.
- Experience with SAP DataSphere at least basic knowledge
Nice to Have
- Knowledge of Power BI
- Familiarity with cloud data ecosystems and integration patterns
- Experience supporting analytics, dashboarding, or self-service BI initiatives
- Exposure to data governance, cataloging, lineage, and semantic modeling concepts
- Understanding of AI/ML data readiness and data consumption patterns
Core Competencies
- Strong ownership and accountability
- Attention to detail and commitment to data quality
- Ability to work independently in a fast-paced, cross-functional environment
- Effective collaboration and communication skills
- Continuous improvement mindset and passion for modern data engineering practices
