Senior Data Engineer - Azure Data Factory and SQL

About this role

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Skills you'll use

What you'll do

• This position develops batch and real-time data pipelines utilizing various data analytics processing frameworks in support of Data Science and Machine Learning practices. This position assists in the integration of data from various data sources, both internal and external. This position performs extract, transform, load (ETL) data conversions, and facilitates data cleansing and enrichment as well as performs full systems life cycle management activities, such as analysis, technical requirements, design, coding, testing, implementation of systems and applications software. This position assists in synthesizing disparate data sources to create reusable and reproducible data assets. This position assists the Data Science community working through analytical model feature tuning.

Who can apply

• Understanding of database systems and data warehousing solutions.
• Understanding of data life cycle stages - data collection, transformation, analysis, storing the data securely, providing data accessibility
• Understanding of the data environment to ensure that it can scale for the following demands: Throughput of data, increasing data pipeline throughput, analyzing large amounts of data, real-time predictions, insights and customer feedback, data security, data regulations, and compliance.
• Contributes to the following: Building a data platform, ensuring data is secure in motion and at rest, automating data compliance and auditing, data warehousing solutions for scalable analytics.
• Familiarity with analytics reporting technologies and environments – (e.g., PBI, Looker, Qlik, etc.)
• Basic knowledge of algorithms and data structures to assist in understanding the big picture of the organization’s overall data function. Knowledge in data filtering and data optimization.
• Familiarity with a Cloud services platform (e.g., GCP, or AZURE, or AWS) and all the data life cycle stages.
• Understanding ETL tools capabilities. Ability to pull data from various sources, perform a load of the transformed data into a database or business intelligence platform.
• Familiarity with Machine learning algorithms which help data scientists make predictions based on current and historical data.
• Knowledge of algorithms and data structures with the ability to organize the data for reporting, analytics, and data mining and perform data filtering and data optimization.

About UPS

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