Skills - Experience and Requirements - A successful Architect will have the following:

Should have prior experience in working as a Data Warehouse/Big Data Architect.

Experience in advanced Apache Spark processing framework, spark programming languages such as Scala/Python/Advanced Java with sound knowledge in shell scripting.

Should have experience in both functional programming and Spark SQL programming dealing with processing terabytes of data.

Specifically, this experience must be in writing Big Data data engineering jobs for large scale data integration in AWS. Prior experience in writing Machine Learning data pipeline using Spark programming language is an added advantage.

Advanced SQL experience including SQL performance tuning is a must.

Should have worked on other big data frameworks such as MapReduce, HDFS, Hive/Impala, AWS Athena.

Experience in logical & physical table design in Big Data environment to suite processing frameworks.

Knowledge of using, setting up and tuning resource management frameworks such as Yarn, Mesos or standalone spark.

Experience in writing spark streaming jobs (producers/consumers) using Apache Kafka or AWS Kinesis is required.

Should have knowledge in variety of data platforms such as Redshift, S3, Teradata, HBase, MySQL/Postgres, MongoDB

Experience in AWS services such as EMR, Glue, S3, Athena, DynamoDB, IAM, Lambda, Cloud watch and Data pipeline.

Must have used the technologies for deploying specific solutions in the area of Big Data and Machine learning.

Experience in AWS cloud transformation projects are required.

Telecommunication experience is an added advantage.