AWS Data Platform Engineer - JHB at Praesignis

Responsibilities:

• Design, build and operationalize large scale enterprise data solutions and applications using one or more of AWS data and analytics services in combination with 3rd parties – Glue, Step-functions, Kafka CC, PySpark, DynamoDB, Delta.io, RedShift, Lambda, DeltaLake, Python.
• Analyze, re-architect and re-platform on-premise data warehouses to data platforms on AWS cloud using AWS or 3rd party services and Kafka CC.
• Design and build production data pipelines from ingestion to consumption within a big data architecture, using Java, PySpark, Scala, Kafka CC.
• Design and implement data engineering, ingestion and curation functions on AWS cloud using AWS native or custom programming.
• Perform detail assessments of current state data platforms and create an appropriate transition path to AWS cloud.
• Design, implement and support an analytical data infrastructure providing ad-hoc access to large datasets and computing power.
• Interface with other technology teams to extract, transform, and load data from a wide variety of data sources using SQL, AWS big data technologies and Kafka CC.
• Creation and support of real-time data pipelines built on AWS technologies including Glue, Lambda, Step Functions, PySpark , Athena and Kafka CC
• Continual research of the latest big data and visualization technologies to provide new capabilities and increase efficiency.
• Working closely with team members to drive real-time model implementations for monitoring and alerting of risk systems.
• Collaborate with other tech teams to implement advanced analytics algorithms that exploit our rich datasets for statistical analysis, prediction, clustering and machine learning.
• Help continually improve ongoing reporting and analysis processes, automating or simplifying self-service support for customers.

Qualifications:

• Bachelor's Degree in Computer Science, Information Technology, or other relevant fields.
• Has experience in any of the following AWS Athena and Glue Pyspark, DynamoDB, Redshift, Lambda, Step Functions and Kafka CC.
• Proficient in AWS Redshift, S3, Glue, Athena, PySpark , Step Functions, Glue Workflows, Kafka CC, Delta.io.
• Knowledge of software engineering best practices across the development lifecycle, including agile methodologies, coding standards, code reviews, source management, build processes, testing, and operations.

Work Experience:

• Advanced working “data engineering” knowledge and experience working with modern data practices, using Delta.io, CDC management and data load practices.
• Experience building/operating highly available, distributed systems of data extraction, ingestion, and processing of large data sets.
• Experience working with distributed systems as it pertains to data storage and computing.
• Experience performing root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement.
• Strong analytic skills related to working with unstructured datasets.
• Build processes supporting data transformation, data structures, meta data, dependency, and workload management.
• A successful history of manipulating, processing and extracting value from large, disconnected data sets.
• Working knowledge of message queuing, stream processing, and highly scalable Big Data, data stores.
• Strong project management and organizational skills.
• Experience supporting and working with cross-functional teams in a dynamic environment.
• Experience in a Data Engineer or similar roles.
• Experience with big data tools is a must: Delta.io, PySpark, Kafka, etc.
• Experience with relational SQL and NoSQL databases, including Postgres and Cassandra.
• Experience with data pipeline and workflow management tools: Step functions, glue workflow etc.
• Experience with AWS cloud services: EC2, EMR, RDS, Redshift.