Design, develop test, deploy, maintain and improve data integration pipeline.
Design, develop, test, deploy, support, enhance data integration solutions seamlessly to connect and integrate enterprise systems in our Enterprise Data Platform.
Innovate for data integration in Apache Spark-based Platform to ensure the technology solutions leverage cutting edge integration capabilities.
Facilitate requirements gathering and process mapping workshops, review business/functional requirement documents, author technical design documents, testing plans and scripts.
Assist with implementing standard operating procedures, facilitate review sessions with functional owners and end-user representatives, and leverage technical knowledge and expertise to drive improvements.
Required Skills
Batch & stream data processing using AWS platform, Snowflake, Apache Spark, EMR, Hadoop, Kafka, KStream, Data as a service (API).
Extremely fluent with Programming : Python, SQL, Shell.
Senior knowledge of ETL tools : Informatica.
Orchestration : Airflow, Nifi, Autosys Must have been part of a Data Migration project from on-premise to cloud using the above technologies.
Excellent articulation and communication skills.
Required Experience
2+ years of Experience with AWS Cloud on data integration with Apache Spark, EMR, Glue, Kafka, Kinesis, and Lambda in S3, Redshift, RDS, MongoDB/DynamoDB ecosystems.
Strong real-life experience in python development especially in pySpark in AWS Cloud environment.
Experience in Python and common python libraries.
Performance & Cost optimization experience for data pipeline in the cloud.
Strong analytical experience with database in writing complex queries, query optimization, debugging, user defined functions, views, indexes etc.
Strong experience with source control systems such as Git, Bitbucket, and Jenkins build and continuous integration tools.
Databricks or Apache Spark Experience is a plus.
Education Requirements
Bachelor’s Degree in Computer Science, Computer Engineering or a closely related field.