Azure Data Engineering Architect
New
L
LingaroData Engineering
PolandContractSenior
Salary not disclosed
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Job Details
- Required Skills
- AWSPythonSQLETLGCPHadoopAzureSparkData modelingPySpark
Requirements
- Proven experience as a Data Engineering Architect or in a similar Data & Analytics role.
- Expertise in designing and implementing scalable data processing frameworks.
- Strong understanding of data modeling, ETL processes, and data pipelines.
- In-depth knowledge of relational databases, NoSQL, data lakes, and data warehouses.
- Experience with big data frameworks such as Hadoop and Spark.
- Proficiency in Python, PySpark, and SQL.
- Familiarity with cloud platforms (Azure, AWS, or GCP) and cloud-based data solutions.
- Knowledge of data governance, privacy, and security best practices.
- Experience leading and mentoring data engineering teams.
- Strong understanding of distributed computing principles including parallel processing.
- Familiarity with agile methodologies and development environments.
- Bachelor's degree in Computer Science, Information Technology, or a related field (Master's preferred).
Responsibilities
- Design and oversee the overall data architecture and infrastructure to ensure scalability and performance.
- Define data models and schemas that address data volume, velocity, and variety requirements.
- Create efficient ETL processes and data pipelines to support data processing needs.
- Select and integrate appropriate data technologies such as data lakes, data warehouses, and big data frameworks.
- Collaborate with stakeholders and data teams to define data requirements and ensure project alignment.
- Provide technical guidance and mentoring to data engineering teams throughout the project lifecycle.
- Conduct performance analysis and optimization to resolve system bottlenecks.
- Oversee the implementation of solutions according to technical specifications.
- Establish data governance strategies, ensuring compliance with privacy and security standards.
- Document architecture and design decisions for knowledge sharing and reference.
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