Sr Azure Data Engineer
Sr Azure Data Engineer
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Job Description
45 years of Data engineering (Both Onprem and Azure) delivery experience including Architecture / analysis / development /
configuration/ deployment.
4 years of implementation experience in Azure Data engineering projects
Mandatory to have knowledge of Big Data Architecture Patterns and experience in the delivery of endtoend Big Data solutions on
Azure Cloud.
Expert in Python/SQL/SparkSQL/PySpark
Expert level understanding on Azure Data Factory Azure Synapse Azure SQL Azure Data Lake Datra bricks perview and Azure App
Extensive handson experience implementing data migration and data processing using Azure services: Serverless Architecture Azure
Storage Azure SQL DB/DW Data Factory Azure Stream Analytics Azure Analysis Service Databricks Azure Data Catalog Cosmos Db
Azure Functions ARM Templates Azure DevOps CI/CD etc.
Expert in at least one distributed data processing framework: Spark (Core Streaming SQL) Storm or Flink etc.
Expert in distributed messaging and ingestion frameworks (Kafka Pulsar Pub/Sub etc) and good to know traditional ETL tools like
Informatica SSIS etc
Should have worked on any NoSQL solutions like Mongo DB Cassandra HBase Cosmos DB etc or Cloudbased NoSQL offerings like
DynamoDB Big Table etc.
Good Exposure in development with CI / CD pipelines. Knowledge of containerization orchestration and Kubernetes engine would be
an added advantage.
Designing and building of data pipelines using API ingestion and Streaming ingestion methods.
Knowledge in Azure Databricks Azure IoT Azure HDInsight Spark Azure Stream Analytics Power BI is desirable.
Strong problem solving and troubleshooting skills.
Experience in working in a fastpaced agile environment.
Strong experience in common data warehouse modelling principles including Kimball Inmon.
Experience with data visualization tools such as Power BI.
Experience in Streaming and batch architecture (e.g. Kafka Kafka streams Spark Flink)
Employment Type
Full Time
Company Industry
Key Skills
- Apache Hive
- S3
- Hadoop
- Redshift
- Spark
- AWS
- Apache Pig
- NoSQL
- Big Data
- Data Warehouse
- Kafka
- Scala
