Checking job availability...
Original
Simplified
- Develop and implement processes and procedures to identify, monitor, and mitigate data risks within the product
- Design and implement complex, scalable solutions to efficiently process data, ensuring consistent and timely delivery and availability
- Focus on building robust systems that can handle large volumes of data with minimal downtime
- Develop solutions using Agile DevOps methodologies and continuous integration/continuous deployment (CI/CD) practices on public cloud platforms
- Create reusable frameworks with a strong emphasis on quality and long-term sustainability
- Collaborate with key partners to enhance understanding of data usage within the business
- Serve as a subject matter expert on the content and application of data in the product and related business areas
- Support the Data Analytics & Insight team by identifying and integrating necessary data into analytics platforms
- Document and enforce requirements for data accuracy, completeness, and timeliness within the product
- Contribute to a team culture that values diversity, equity, inclusion, and respect
- Bachelor's degree in Computer Science, Engineering, Information Systems, or related disciplines
- Minimum 3 years of experience in data engineering
- Working understanding of NoSQL databases
- Professional experience in an agile, dynamic, and customer-facing environment
- Recent hands-on experience actively coding as a data engineer (back-end software engineer considered)
- Understanding of distributed systems and cloud technologies (AWS, GCP, Azure, etc.)
- Understanding of data streaming and scalable data processing frameworks (Kafka, Spark Structured Streaming, Flink, Beam, etc.)
- Experience with SQL (any dialect) and data tools (e.g., Dbt, Glue)
- Experience in all stages of the software development lifecycle (requirements, design, architecture, development, testing, deployment, release, and support)
- Experience with large-scale datasets, data lake, and data warehouse technologies on at least TB scale (ideally PB scale) with Redshift, Snowflake, or BigQuery.
- Experience in Infrastructure as Code (ideally Terraform) for cloud-based data infrastructure and extensive knowledge of Python or good experience with a JVM language
- Experience with a scheduling system (Airflow etc.)
- Understanding of (distributed and non-distributed) data structures, caching concepts, CAP theorem
- Understanding of security frameworks / standards and privacy
- Experience in automating deployment, releases and testing in continuous integration, continuous delivery pipelines is desired
- A solid approach to writing unit level tests using mocking frameworks, as well as automating component, integration and end-to-end tests
- Experience with containers and container-based deployment environment (Docker, Kubernetes, etc.)
- Develop and implement processes and procedures to identify, monitor, and mitigate data risks within the product
- Design and implement complex, scalable solutions to efficiently process data, ensuring consistent and timely delivery and availability
- Focus on building robust systems that can handle large volumes of data with minimal downtime
- Develop solutions using Agile DevOps methodologies and continuous integration/continuous deployment (CI/CD) practices on public cloud platforms
- Create reusable frameworks with a strong emphasis on quality and long-term sustainability
- Collaborate with key partners to enhance understanding of data usage within the business
- Serve as a subject matter expert on the content and application of data in the product and related business areas
- Support the Data Analytics & Insight team by identifying and integrating necessary data into analytics platforms
- Document and enforce requirements for data accuracy, completeness, and timeliness within the product
- Contribute to a team culture that values diversity, equity, inclusion, and respect
- Bachelor's degree in Computer Science, Engineering, Information Systems, or related disciplines
- Minimum 3 years of experience in data engineering
- Working understanding of NoSQL databases
- Professional experience in an agile, dynamic, and customer-facing environment
- Recent hands-on experience actively coding as a data engineer (back-end software engineer considered)
- Understanding of distributed systems and cloud technologies (AWS, GCP, Azure, etc.)
- Understanding of data streaming and scalable data processing frameworks (Kafka, Spark Structured Streaming, Flink, Beam, etc.)
- Experience with SQL (any dialect) and data tools (e.g., Dbt, Glue)
- Experience in all stages of the software development lifecycle (requirements, design, architecture, development, testing, deployment, release, and support)
- Experience with large-scale datasets, data lake, and data warehouse technologies on at least TB scale (ideally PB scale) with Redshift, Snowflake, or BigQuery.
- Experience in Infrastructure as Code (ideally Terraform) for cloud-based data infrastructure and extensive knowledge of Python or good experience with a JVM language
- Experience with a scheduling system (Airflow etc.)
- Understanding of (distributed and non-distributed) data structures, caching concepts, CAP theorem
- Understanding of security frameworks / standards and privacy
- Experience in automating deployment, releases and testing in continuous integration, continuous delivery pipelines is desired
- A solid approach to writing unit level tests using mocking frameworks, as well as automating component, integration and end-to-end tests
- Experience with containers and container-based deployment environment (Docker, Kubernetes, etc.)