Job Description

Locations

• San Francisco Bay Area, California, United States

Salary

Skills Required

• using data to drive business decisionsintermediate
• generate ideasintermediate
• encourage collaboratorsintermediate
• build and maintain analytic data pipelinesintermediate
• leverage large and sophisticated data sourcesintermediate
• deliver data productsintermediate
• partner with multi-functional teamsintermediate
• identify, develop, and maintain metricsintermediate
• design, create, refine, and maintain data pipelinesintermediate
• design, create, refine, and maintain ingestion processesintermediate
• modelingintermediate
• analysisintermediate
• reportingintermediate
• collaborate with data scientists, analysts, and engineersintermediate
• build full-service data solutionsintermediate
• work with multi-functional business partners and vendorsintermediate
• acquire and transform raw data sourcesintermediate
• develop deep understanding of retail customer baseintermediate
• develop tools to improve business efficiency and productivityintermediate

Required Qualifications

• 4+ years of industry experience building analytics pipelines in a production development environment. (experience, 4 years)
• Bachelor’s degree in Computer Science, Electrical Engineering, Information Systems, Data Science or other Analytics fields, or a related subject area, or equivalent industry experience. (experience)
• 4+ years experience coding in SQL or PySpark and Python. (experience, 4 years)
• 4+ years in data modeling and building robust and scalable data processes and pipelines for modeling, analysis, and reporting. (experience, 4 years)
• Ability to initiate, refine, and complete projects with minimal guidance. (experience)
• Ability to think critically and collaborate multi-functionally with other data engineering, data science and analytics partners distilling abstract requirements into clear data products. (experience)

Preferred Qualifications

• 6+ years of experience building analytics pipelines in production development environment, ideally with Snowflake SQL or PySpark, and pipeline tools like Airflow and dbt. (experience, 6 years)
• 3+ years of experience in data observability building unit tests, anomaly detection and alerting solutions. (experience, 3 years)
• 3+ years of experience developing feature stores for machine learning applications, testing and evaluating machine learning models and scaling and deploying ML models. (experience, 3 years)
• Experience translating business needs into logic and key performance indicators. (experience)
• Experience with BI processes and some experience with dashboard tools like Tableau. (experience)
• Master's Degree in Computer Science, Electrical Engineering, Information Systems, Data Science or other Analytics field, or a related subject area with specialization in Machine Learning. (degree in computer science)

Responsibilities

• You will partner with multi-functional teams to identify, develop, and maintain metrics and data pipelines to support analytics, reporting, and key decisions on a wide variety of topics including product launches, customer experience, and operational performance. Other responsibilities include the following:
• - Design, create, refine, and maintain data pipelines and ingestion processes used for modeling, analysis, and reporting.
• - Collaborate with other data scientists, analysts, and engineers to build full-service data solutions.
• - Work with multi-functional business partners and vendors to acquire and transform raw data sources.
• - Develop a deep understanding of our retail customer base and purchase choices and give to the development of tools to improve business efficiency and productivity.
• Work with large volumes of data; extract and manipulate large datasets using tools such as SQL, dbt, command line and scripting languages.
• Analyze data covering a wide range of information from financial reporting to device purchase patterns such as export and trader activity and identify new patterns through data mining.
• Design, develop, validate and maintain big data-driven predictive models, tools and pipelines to improve device export predictions using the newest technologies in machine learning.
• Use data observability tools to develop and manage and automate testing, alerting and improve data quality.
• Collaborate with data science, product, and engineering groups to translate business needs into data requirements, work with production teams to handoff reporting requirements for data products and communicate sophisticated concepts and the results of the analyses in a clear and effective manner to business partners.
• Strong interactive data engineering experience, including using version control, release management, requirements gathering, data testing and documentation.

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