The company is looking to build innovative solutions that provide a comprehensive understanding of complex market dynamics, empowering leaders to optimize strategies and unlock new growth opportunities. The goal is to develop cutting-edge data products that drive measurable impact for a leading brand in its industry.
Requirements
- Expert-level SQL skills (6+ years), including ad-hoc querying.
- Proficiency in Python (6+ years), including standard libraries.
- Experience with Apache Spark (2-4 years).
- Experience with Amazon Web Services (AWS).
- Experience with Databricks.
- Experience with Spark SQL (2-4 years).
- Experience with Pyspark (2-4 years).
Responsibilities
- Design, develop, and program advanced methods and systems to consolidate and analyze structured and unstructured “big data” sources.
- Build innovative data products for comprehensive analysis, generating actionable insights and solutions.
- Develop and code software programs, algorithms, and automated processes to cleanse, integrate, and evaluate large datasets from multiple disparate sources.
- Identify meaningful insights from vast data and metadata sources, interpreting and communicating findings to product, service, and business managers.
- Validate key performance indicators and build queries to quantitatively measure business performance.
- Create SQL queries and data visualizations to fulfill ad-hoc analysis requests and ongoing reporting needs.
- Utilize data mining techniques to extract information from data sets and identify correlations and patterns.
Other
- 7-month hybrid engagement in Beaverton, OR
- Collaborate with product and service teams to identify critical questions and issues for data analysis and experimentation.
- Interpreting and communicating findings to product, service, and business managers.
- Lead the accomplishment of key goals across consumer and commercial analytics functions, working with stakeholders to develop sustainable data solutions and provide recommendations.
- Communicate with cross-functional teams to understand the business causes of data anomalies and outliers.
