Data Engineering- SSEII

Job Summary

• Operationalize our Databricks Lakehouse: Develop and maintain a unified data platform using Azure Databricks, enabling efficient data storage, processing, and analysis across the organization.
• Build Data Pipelines: Design, develop, and maintain scalable data pipelines; establish API integrations for efficient data transfer. Implement ETL processes and ensure data integrity and quality.
• Deploy Data Technology: Utilize technologies such as Spark, Kafka, and Airflow to manage large-scale data processing. Test, monitor and troubleshoot systems to ensure smooth operation.
• Collaborate: Work closely with data engineers, analysts, and scientists to understand their needs and optimize the performance of data workflows

What You'll Bring

• Development Experience: 5+ years with Python, Scala, Java, or C#. Experience with Python is preferred.
• Data Engineering Tools: 5+ years with Databricks, Snowflake, BigQuery, Apache Spark, HIVE, Hadoop, Cloudera, or RedShift. Experience with PySpark on Databricks is preferred.
• Data Architecture: Proficiency in high-performance data pipeline design and mastery of pipeline guarantees, such as Idempotency, At-Least-Once Processing, Exact-Once Processing, Fault Tolerance, Eventual Consistency, Streaming Consistency, Transactional Consistency, and Observability.
• Containerization: Experience developing in a containerized environment like Docker, Rancher, or Kubernetes.
• Data Orchestration: Experience with Apache Airflow (or similar tool) for orchestrating data processing jobs.
• Education: Bachelors degree in Computer Science (or related field) or equivalent combination of education and experience.

Educational Qualification and Experience


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