Job purpose description
Support in providing infrastructure, tools and frameworks used to deliver end-to-end solutions to business problems. Build scalable infrastructure for supporting the delivery of business insights from raw data sources with a focus on collecting, managing, analyzing, visualizing data and developing analytical solutions.
Responsible for expanding and optimizing the organizations data and data pipeline architecture, whilst optimizing data flow and collection to ultimately support data initiatives
8 - 10 years experience
Experience Description: Experience with big data tools: Hadoop, Spark, Kafka, etc. Experience with relational SQL and NoSQL databases, including Postgres and Cassandra. Experience with data pipeline and workflow management tools: Azkaban, Luigi, Airflow, etc. Experience with AWS cloud services: EC2, EMR, RDS, Redshift. Experience with stream-processing systems: Storm, Spark-Streaming, etc. Experience with object oriented/object function scripting languages: Python, Java, C++, Scala, etc
Experience Description: Working SQL knowledge and experience working with relational databases, query authoring (SQL) as well as working familiarity with a variety of databases. Experience building and optimizing ‘big data’ data pipelines, architectures and data sets. Experience performing root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement.
Experience Description: Strong analytic skills related to working with unstructured datasets. Build processes supporting data transformation,
data structures, metadata, dependency and workload management. A successful history of manipulating, processing and extracting value from large disconnected datasets. Working knowledge of message queuing, stream processing, and highly scalable ‘big data’ data stores.
Provide Data engineering guidance, information services and ensure an effective data engineering capability, works closely with data analysts and data scientists to ensure and effective data team. Collaborate with technology and project teams.
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