Purpose:

We are looking for a highly skilled Data Engineer to design, construct, install, test, and maintain scalable data management systems. You will ensure data accessibility, reliability, and efficiency for end users by developing data set processes and optimizing data retrieval.

Knowledge & Experience:

Experience: 3-7 years in a data engineering role with a strong background in data integration and management.

Education: Bachelor’s degree in Computer Science, Engineering, Information Technology, or a related field.

Required Skills:

Strong experience in databases, ETL processes, data modeling, and data warehouses.

Proficiency in dimensional modeling.

Expertise in Cloud technologies (Snowflake, AWS, Azure), DBMS (Oracle / MSSQL), Python, Docker, and Kubernetes.

If required, PL/SQL knowledge is mandatory.

Preferred Skills:

Experience with data warehouse tooling such as ETL tools, data profiling tools, data catalogs, and data modeling tools.

Familiarity with Scrum methodologies.

Knowledge of data profiling, data modeling, data catalogs, and metadata management tools.

Work Approach: Ability to work independently with general supervision.

Key Responsibilities:

Data Integration: Integrate data from multiple sources into the data warehouse (DWH). Design, implement, and test ETL and supporting data management processes.

Data Pipelines: Develop and deploy data pipelines and assist developers with Python code.

Data Modeling: Design and implement (dimensional) data models to support efficient querying and analysis.

Performance Optimization: Optimize DWH performance for faster query execution.

Testing & Automation: Develop automated tests for continuous integration and deployment processes.

Data Quality & Governance: Establish data profiling and quality processes to ensure accuracy, consistency, and reliability.

Support & Maintenance: Provide support for data-related issues and monitor DWH performance.

Dimensions of the Role:

Direct Responsibility: Ensuring operational efficiency and reliability of data systems.

Accountability: Data integrity, compliance with departmental standards, and adherence to quality controls.

Financial Responsibility: Managing budget allocations for data storage and processing.

Personal Competencies:

Strong problem-solving skills and ability to think algorithmically.

Effective communication skills for collaboration with technical and non-technical teams.

Detail-oriented with a commitment to high-quality data practices.

Adaptability to rapidly changing technologies and environments.

Proactive, self-driven, and capable of managing multiple projects.

Key Relationships:

Reports to: Corporate Data Manager

Collaborates with: Data Scientists, Analytics Teams, IT Support Teams

Key Customers: Internal departments requiring data for decision-making, external partners for data exchange initiatives.