TTC Group
Data & ML Engineer (L5)
Key Responsibilities:
Work with customers focusing on AWS Analytics and ML service offerings (Amazon Kinesis, AWS Glue, Amazon Redshift, Amazon EMR, Amazon Athena, Amazon Sagemaker) Help customers leverage their data to develop business insights Create white papers, write blogs, build demos and other reusable collateral Work closely with Solution Architects, Data Scientists and Service Engineering teams Experience with design, development and operations leveraging Amazon Kinesis, Apache Kafka, Apache Spark, Amazon Sagemaker, Amazon EMR, NoSQL technologies Develop and define key business questions and build data sets that answer those questions Basic Qualifications:
Bachelor’s degree, or equivalent experience, in Computer Science, Engineering, Mathematics or related field 5 years’ experience of Data platform implementation, including 3 years hands-on with Kinesis/Kafka/Spark/Storm implementations Experience with analytic solutions applied to Marketing or Risk needs of enterprises Basic understanding of machine learning fundamentals Ability to take Machine Learning models and implement them as part of data pipeline 5 years of IT platform implementation experience Experience with relevant tools (Flink, Spark, Sqoop, Flume, Kafka, Amazon Kinesis) Experience developing software code (Java, JavaScript, Python) Current hands-on implementation experience required Preferred Qualifications:
Masters or PhD in Computer Science, Physics, Engineering or Math Hands on experience with large-scale data science/data analytics projects Hands-on experience with Data Analytics technologies (AWS, Hadoop, Spark, Spark SQL, Mlib or Storm/Samza) Implementing AWS services in distributed computing environments Experience with modern distributed Machine Learning and Deep Learning frameworks
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Work with customers focusing on AWS Analytics and ML service offerings (Amazon Kinesis, AWS Glue, Amazon Redshift, Amazon EMR, Amazon Athena, Amazon Sagemaker) Help customers leverage their data to develop business insights Create white papers, write blogs, build demos and other reusable collateral Work closely with Solution Architects, Data Scientists and Service Engineering teams Experience with design, development and operations leveraging Amazon Kinesis, Apache Kafka, Apache Spark, Amazon Sagemaker, Amazon EMR, NoSQL technologies Develop and define key business questions and build data sets that answer those questions Basic Qualifications:
Bachelor’s degree, or equivalent experience, in Computer Science, Engineering, Mathematics or related field 5 years’ experience of Data platform implementation, including 3 years hands-on with Kinesis/Kafka/Spark/Storm implementations Experience with analytic solutions applied to Marketing or Risk needs of enterprises Basic understanding of machine learning fundamentals Ability to take Machine Learning models and implement them as part of data pipeline 5 years of IT platform implementation experience Experience with relevant tools (Flink, Spark, Sqoop, Flume, Kafka, Amazon Kinesis) Experience developing software code (Java, JavaScript, Python) Current hands-on implementation experience required Preferred Qualifications:
Masters or PhD in Computer Science, Physics, Engineering or Math Hands on experience with large-scale data science/data analytics projects Hands-on experience with Data Analytics technologies (AWS, Hadoop, Spark, Spark SQL, Mlib or Storm/Samza) Implementing AWS services in distributed computing environments Experience with modern distributed Machine Learning and Deep Learning frameworks
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