资深工程师-IT

Lead moderately complex projects from technical design through completion. Duties include: Act as an on-call SME resource to manage admin failure and messaging events; Test and execute pre-defined ETL processes on standard data sets; Assist the research, troubleshooting, and resolution of data issues impacting extract delivery.; Understand requirements regarding solutions, ensure data quality and match-back to core systems transaction results; Perform data validation, unit testing and troubleshooting for data model usability; Support data architecture development and optimizes data pipelines to store, integrate and process high-volume data sets; Perform code reviews for others in project; Collaborate with Data Scientists to ensure architectural and project specification needs are being met; Conduct necessary data transformations to populate data into a warehouse table structure that is optimized for reporting; Isolate and define core reusable data assets that support the long-term business strategy for development efforts; Review data analysis and data mapping logic; Identify recurring causes for defects and works proactively to resolve issues; Review fixes with MDM/ Data Governance teams; Identify methods for collecting data and perform data analyses; Communicate analytical findings, including through reports, briefings, presentations, and data visualization. Telecommuting permitted. Position requires 10% domestic travel. Requirements: Employer will accept a Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, Electronics Engineering or related field and 72 months of experience in experience in the job offered or in a related occupation.
Position requires:

• Python;

• Azure Cloud Platform services for data processing such as Databricks or Data Factory;

• CICD pipelines built on Azure DevOps;

• Building Integration with SAP systems such as BODS or HANA;

• SAP HANA data objects and underlying data models;

• Data architectures involving Delta Lake, Lakehouse, data warehouse, and ODS;

• Data modeling tools such as ER or Studio Data Architect;

• PySpark and SQL for data processing, analysis and data engineering;

• Kafka;

10. ETL Development using SSIS, Informatica; 11. Linux shell scripting and Powershell; 12. Master Data Management and Data Governance; and 13. Data Visualization and Reporting.
Originally posted on Himalayas