Experience in building and productionizing big data architectures, pipelines and data sets.
Understanding data concepts and patterns of big data, data lake, lambda architecture, stream processing, DWH, BI & reporting.
Min. 2+ years of experience in a Data Engineer role, who has attained experience using the following software/tools:
Experience with big data tools like Hadoop, Spark, Kafka, etc.
-Experience with object-oriented / functional / scripting languages like Python, Scala, Java, R, C++, Bash, PowerShell etc.
-Experience with MS Azure (Databricks, Data Factory, Data Lake, Azure SQL, Event Hub, etc.) or AWS (Glue, EC2, EMR, RDS, Redshift, Sagemaker, etc.) cloud services.
-Implementing large-scale data/events oriented pipelines/workflows using ETL tools.
-Extensive working experience with relational (MS SQL, Oracle, Postgres, Snowflake, etc.) and NoSQL databases (Cassandra, MongoDB, Elasticsearch, Redis, etc.)

Other skill:

Strong analytic skills related to working with structured and unstructured datasets.
Build processes supporting data transformation, data structures, metadata, dependency and workload management
Experience in setting up and using CI/CD automation tools like Azure DevOps, AWS CodePipeline, etc.
Person who is precise, well organized, has good communication skill, can adapt to changing circumstances and is not afraid of responsibility for his / her work will do great in this role.
Deep hands-on development experience in MS Azure or AWS environments
Past experience in delivery of business intelligence projects, using tools like Power BI, Tableau, Qlik Sense, Keboola
Working knowledge of message queuing, stream processing, and highly scalable real-time data processing using technologies like Storm, Spark-Streaming, etc.
Experience with data pipeline / workflow management tools like AWS Glue, Azure Data Factory, Airflow, AWS Step Functions, NiFi, etc.