Microsoft Senior Data Engineer 

United States, Washington 

903639464

ata engineer, you willbe responsible fordesigning and executing paymentwide data architecture. You willset the stage for payments to be a datacart performance, subscription churn rates,payment approval rates,payment anomalies, Artificial Intelligence/Copilot models, and more. You willall current payment data models including the ingestion and processing pipelines and outputs.

track recordworking in data engineering roles that marry user experience, platform capabilities with requirementsambiguity, FinTechIn addition to the data platform development work, the position also includes building and

Required Qualifications

• Bachelor's Degree in Computer Science, Math, Software Engineering, ComputerEngineering ,or related field AND 2+years experiencein business analytics, data science, software development, data modeling or data engineering work
  • ORMaster's Degree in Computer Science, Math, Software Engineering, Computer Engineering or related field AND 1+ year(s) experience in business analytics, data science, software development, or data engineering work
  • OR equivalent experience.

Preferred Qualifications

• Meaningful experience in following technologies:Python, SQL
• Experience and interest in Cloud platforms such asAzure (preferred)or AWS
• Experience in Distributed Processing usingApache Spark
• Experience in creating data structuresoptimizedfor storage and various query patterns for e.g., Parquet andDelta Lake
• Meaningful experience in at least one database technology such as:
  • Traditional RDBMS (MS SQL Server, Oracle)
  • NoSQL (MongoDB, Cassandra, Neo4J, Cosmos DB, Gremlin)

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:Microsoft will accept applications for the role until June 24, 2024.

Data Requirements and Modeling

• appropriate stakeholdersand records and documents data requirements. Evaluates project plan to understand data costs, access, usage, use cases, and availability for business or customer scenarios related to a product feature.
• Contributes to theappropriate datamodel for the project and drafts design specification documents to model the flow and storage of data for specific parts of a data pipeline. Works with senior engineers andappropriate stakeholders(e.g., Data Science Specialists) to contribute basic improvements to design specifications, data models, or data schemas, so that data is easy to connect, ingest, has a clear lineage, and is responsive to work with.
• Participates in code reviews and provides constructive feedback to team members. Uses knowledge ofone or more usecases to implement orchestration techniques that automate data extraction logic from one source to another. Uses basic data protocols and reduction

Data Management and Transformation

• Assesses data quality and completeness using queries, data wrangling, and basic statistical techniques. Helps others merge data into distributed systems, products, or tools for further processing.
• Designs andmaintainsassigned data tools used to transform, manage, and access data.Writes efficient code to test andvalidatestorage and availability of data platforms and implements sustainable design patterns to make data platforms more usable and robust to failure and change. Works with others to analyze relevant data sources that allow others to develop insights into data architecture designs or solution fixes.

Operational Excellence

• Follows existing documentation to implement performance monitoring protocols across a datapipeline. Therebyimproves on the currentprocesses for metric collections and evangelizes itacross the team.
• Performs root cause analysis in response to detected problems/anomalies toidentifythe reason for alerts and implement basic solutions that minimize points of failure. Implements and monitors improvements across assigned product feature toretaindata quality andoptimalperformance (e.g., latency, cost)

throughout the data lifecycle.

• Providesdata-based insights into the health of data products owned by the team according to service level agreements (SLAs) across assigned features.
• Embody our