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    Data Engineer Analyst - Toronto, Canada - CB Canada

    CB Canada
    CB Canada Toronto, Canada

    Found in: Talent CA 2 C2 - 1 week ago

    Cb Canada background
    Description

    Analyze and Design data analytics solutions for business problems

    1. Analyze sample data sets given by the client to understand the size and type of data (structured or unstructured, text, image, video). Then provide options on the suitable big data frameworks and databases to use.
    2. Survey business users and IT teams to identify, analyze, collect, transform, and document data sets that can be used to drive business insights. Extract, transform and organize the data into datasets using tools like SQL
    3. Design the data pipelines so data can be used for Data Science, Machine Learning, Artificial Intelligence, Operational Research techniques. Drive personalization, real-time decision-making, causal inference and predictive analytics capabilities through the application of Machine Learning, Deep Learning, simulation and AI processes in an agile development framework.


    Build quantitative programs using latest analytics tools

    1. Build large scale fault tolerant enterprise applications using Hadoop and Big Data open source solutions such as: MR, Hive, Pig, HBase, Spark
    2. Define and develop data pipelines and work on end to end holistic data solutions that include OLTP data stores (SQL data stores and others) , NoSQL data stores (Cassandra and others ), messaging and data movement products (such as Kafka, ActiveMQ, and others), search engine products (such as Elastic Search, and others), as well as OLAP data stores (such as Hadoop, HBase, Teradata, and others).
    3. Develop new data sources by analyzing similar use cases from the Tiger's case studies and accelerator PoCs. Simulate market scenarios using Python or other programming tools to create and evaluate new additions to enhance data assets.
    4. Work with multi-functional teams to access data elements, understand the data being analyzed, and identify improvement opportunities for data ingestion process.
    5. Develop repeatable testing strategies for measuring results produced by analytics models.
    6. Test solution algorithms on real-time data processing systems and benchmark performance using R, Python, etc.
    7. Validate the results of the model by developing testing framework using Hadoop, Python, etc.
    8. Operationalizing an enterprise data governance strategy. Aligning policies and processes to support data strategy, data quality, regulations, and latency.

    Support Data Analytics platforms

    1. Work with client's IT stakeholders to deploy the developed solution and make sure it can be implemented in production by the client engineering team
    2. Support all data collection, validation, and analysis activities including risk, spend, commodity and supplier information.
    3. Deliver datasets with the appropriate characteristics for the desired usage patterns and use cases.
    4. Benchmark the developed solution for data consistency, data latency, data quality and deploy them into production.
    5. Support & optimize the existing data analytics platforms using Big data tools


    Job Requirements:

    Education Qualification: Bachelor's Degree


    *Salaries may increase annually based on performance. Additional bonuses may be issued at the company's discretion and comparative market trends. If issued, the bonus may be in the form of Cash or ESOPS. COLA may be added as well at the company's discretion.

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