Engineer III, Data Engineering

Position Summary

Design and develop scalable data stores and frameworks with sub-second query latency on highly multidimensional data.

Role and Responsibilities

Design and develop scalable data stores and frameworks with sub-second query latency on highly multidimensional data. Provide engineering solutions to aggregate and automate large scale data flows from varying sources. Build real time streaming pipelines that deliver data with measurable quality under the SLA. Deliver products with top notch quality in a fast-paced environment. Contribute towards building a system with a test-driven development/agile approach. Collaborate with other team members in breaking down tasks and implementation of the initiatives all the way to release.

Skills and Qualifications

Master's degree in Business Analytics, Mathematics, Statistics, Data Science, Mechanical Engineering, Computer Applications, a related field, or a foreign equivalent plus 2 years of postbaccalaureate experience in job offered or related role. In Lieu of a Master's degree and 2 years of experience, Employer is willing to accept a Bachelor's degree in Business Analytics, Mathematics, Statistics, Data Science, Mechanical Engineering, Computer Applications, a related field, or a foreign equivalent plus 5 years of progressively responsible post-baccalaureate experience in job offered or related role. Applicants must have 2 years of experience (5 years in possession of a relevant Bachelor's degree) in the following: (1) utilizing Apache Spark (EMR) for distributed data processing at a large scale (PySpark); (2) running interactive SQL queries(Spark SQL); (3) Extracting and analyzing data from databases using SQL queries to find relevant insights and customer-specific aggregated results; (4) building ETL pipelines to collect, process, and integrate structured and semi-structured data from various data producers into Snowflake; (5) AWS Glue/Lambad to populate Data Catalog with metatable definitions and run scalable serverless data processing jobs as a part of ETL operations; (6) creating data and dimensional models, design Entity Relationships, and building scalable data pipelines to build Enterprise Data Warehouse; (7) utilizing AWS as an infrastructure service (IAAS) to build frameworks for data ingestion, transformation, and orchestration of data pipelines; and (8) building data pipelines DAG's, schedules, operators, connections, and hooks for data orchestration and optimize data workflows using Apache Airflow.

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