Data Engineer

Key Skills

AWS, Azure, Cloud computing, Continuous Integration & Continuous Delivery - CI/CD, Data Lake, Data Warehousing, Databricks, Effective communication skills, ETL frameworks, Java Programming, Machine learning techniques, Program Management, Python Programming, Scala Programming, Snowflake, SQL

Job Description

In alignment with our Microsoft values, we are committed to cultivating an inclusive work environment for all employees to positively impact our culture every day.

Cloud Operations + Innovation (CO+I) is the team behind one of the World’s largest Cloud Infrastructures, responsible for powering all Microsoft online Products and Services as well as powering Microsoft’s “Cloud First” mission. Our focus is on smart growth, high efficiency, and delivering a trusted experience to customers and partners worldwide, and we are looking for passionate, high-energy program managers to help achieve that mission. 

If you want to have fundamental impact on how Microsoft’s Cloud is deployed and operated globally, want to be involved with one of the most strategically important teams at Microsoft, have strong technical and business acumen, leadership, creativity, and out of the box thinking are some of the skills you bring to the table - this job is for you! 

We are looking for an exceptional data engineer whose main task is to collaborate with our engineering team and build solutions that improve our Data Center delivery cycle time. As a data engineer, you will partner with various teams to develop business requirements to standardize the data input and optimize algorithms for data quality check.

In alignment with our Microsoft values, we are committed to cultivating an inclusive work environment for all employees to positively impact our culture every day.

Qualifications

Qualifications:

• Relevant field such as computer science or information science required
• 3+ years of relevant work experience (an equivalent combination of education and experience may be considered)
• Excellent verbal and written communication skills
• Excellent knowledge of software engineering fundamentals
• Strong SQL skills
• Strong programming skills with Python, Java, or Scala
• Demonstrated experience in working with:
• Data storage solutions in an administrative capacity
• SCM solutions (Bitbucket/Github/Gitlab)
• Data technologies (e.g., Snowflake, Databricks, Palantir)
• Deep understanding of data management, administration, security, and access control processes and implementation
• Experience and desire to set up data capabilities and platforms as well as managing them
• Self-organizing and self-starting
• Continuous learner, independent worker, and strong decision maker (with minimal supervision)
• Collaborative teammate with dedication to Diversity, Equity, and Inclusion

Preferred Qualifications

• Experience with Cloud-based services (Azure/AWS)
• Experience with serving business intelligence feeds
• Experience with Azure, Snowflake, and Databricks administration and Data Lakes
• Understanding of CI/CD practices

The ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include, but are not limited to, the following specialized security screenings: Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter.

Responsibilities

• Bring, build, and drive data culture and best practices, enabling the scheduling and data science org to build better, more reliable, and secure data pipeline and data-driven products and powering use cases spanning internal analytics and data science / ML needs
• Use tooling and automation to deliver a developer experience that enables teams to quickly and easily build out data products following mature principles
• Partner with Program Managers and Data Scientists to build pipelines from our data warehouse to power crucial customer and product data requirements in our internal tools
• Define processes and ETL infrastructure to transform and make data readily available across the team
• Implement automated workflows that lower manual/operational cost for stakeholders, define and uphold SLAs for timely delivery of data, and move the company closer to data democratization