Cloud Data Engineer implementing tailored solutions for Volkswagen Group data processing. Building ETL/ELT pipelines while collaborating with technical experts.
About the role
- Data Engineer leading data foundation architecture and optimization for a Kenyan startup. Constructing data pipelines that fuel machine learning models and internal analytics.
Responsibilities
- Architect and sustain scalable ETL workflows, guaranteeing consistency and accuracy across diverse data origins.
- Refine and optimize data models and database structures specifically tailored for reporting and analytics.
- Enforce industry best practices regarding data warehousing and storage methodologies.
- Fine-tune data systems to handle the demands of both real-time streams and batch processing.
- Manage the cloud data environment, utilizing platforms such as AWS, Azure, or GCP.
- Coordinate with software engineers to embed data solutions directly into our product suite.
- Design robust processes for ingesting both structured and unstructured datasets.
- Script automated quality checks and deploy monitoring instrumentation to instantly detect data anomalies.
- Build APIs and services that ensure seamless data interoperability between systems.
- Continuously monitor pipeline health, troubleshooting bottlenecks to maintain an uninterrupted data flow.
- Embed data governance and security protocols that meet rigorous industry standards.
- Collaborate with data scientists and analysts to maximize the usability and accessibility of our data assets.
- Maintain comprehensive documentation covering schemas, transformations, and pipeline architecture.
- Keep a pulse on emerging trends in cloud tech, analytics, and data engineering to drive continuous improvement.
Requirements
- A minimum of 3 years of professional experience in Data Engineering or a similar technical role.
- Bachelor’s or Master’s degree in Engineering, Computer Science, Data Science, or a relevant discipline.
- Expert-level command of SQL and management systems like PostgreSQL or MySQL.
- Hands-on proficiency with pipeline tools such as Luigi, DBT, or Apache Airflow.
- Practical experience with heavy-lifting technologies like Hadoop, Spark, or Kafka.
- Proven skills with cloud data stacks, specifically Google BigQuery, AWS Redshift, or Azure Data Factory.
- Strong programming logic in Java, Scala, or Python for data processing tasks.
- Familiarity with data integration frameworks and API utilization.
- Understanding of security best practices and compliance frameworks.
Benefits
- Flexible work arrangements
- Professional development opportunities
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
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