DTS R&D Lead Data Engineer

DTS R&D Lead Data Engineer

Job Description

You were made to do this work: designing new technologies, diving into data, optimizing digital experiences, and constantly developing better, faster ways to get results.

You want to be part of a performance culture dedicated to building technology for a purpose that matters.

You want to work in an environment that promotes sustainability, inclusion, wellbeing, and career development.

In this role, you’ll help us deliver better care for billions of people around the world.  It starts with YOU. 

In this role, you will:


* Understand business problems, analyze data, and define success criteria.


* Work with engineering team and architecture teams for data identification and collection, harmonization, and cleansing for the data analysis and preparation.


* Responsible for analyzing and identifying appropriate algorithms for the defined problem statement.


* Analyze additional data inputs and methods that would improve the results of the models and look for opportunities.


* Responsible for building models that are interpretable, explainable and sustainable at scale and meets business needs.


* Build visualizations and demonstrate the results of the model to the stakeholders and leadership team.


* Must be conversant with Agile methodologies and tools and have a track record of delivering products in a production environment.


* Lead the design of prototypes in our AI factory, partnering with product teams, AI strategists, and other stakeholders throughout the AI development life cycle.


* Lead and transform data science prototypes.


* Mentor a diverse team of junior engineers in machine learning techniques, tools and concepts.

Provides guidance and leadership to junior engineers.


* Work with Technical Architects, Product Owners and Business teams to translate requirements into technical design for data modelling and data integration.


* Demonstrate deep background in data warehousing, data modelling and ETL/ELT data processing patterns.


* Design and develop ETL/ELT pipelines with reusable patterns and frameworks.


* Design and build efficient SQL to process and curate the data sets in Azure, Databricks, and Snowflake.


* Design and review data ingestion frameworks leveraging Python, Spark, Azure Data Factory, Snowpipe, etc.


* Design and build Data Quality models and ABCR frameworks to ingest, validate, curate and prepare the data for consumption.


* Understand the functional domain, business needs and able to identify the gaps in the requirements proactively prior to implementing solutions.


* Work with platform teams to design and build processes for automation in pipeline build, testing and code migrations.


* Demonstrate exceptional impact in delivering projects, products and/or platforms in terms of scalable data processing and application architectures, technical deliverables and delivery throughout the project l...