Data Product Analyst, Signals
New
Y
YipitDataMarket Research, Analytics
Fully remotely within the United StatesFull-TimeMiddle
Salary170000 USD per year
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Job Details
- Experience
- 4–6 years of experience
- Required Skills
- PythonSQLDatabricksGitHubPySpark
Requirements
- 4–6 years of experience in data analytics, data operations, consulting, or a related field
- 4+ years of experience working with SQL, Python, Pyspark, or other programming languages to explore and transform large, complex datasets
- Familiar with GitHub and Databricks
- Experience using sample or panel data to make precise estimates that guide decision-making
- Scrappy and resourceful
- Thrive in ambiguity and fast-paced environments
- Excellent attention to detail and a natural instinct for spotting data anomalies
- Communicate clearly and concisely—both in writing and verbally
- Intellectually curious about technology markets, business trends, and how data is used to influence decision-making
- Take ownership of problems and drive them to completion without constant oversight
Responsibilities
- Become a core owner of Signals data products – help prioritize and vet new data sources and design their incorporation into our pipelines and platform
- Work closely with our AI and Data Engineering teams to build and maintain cutting-edge AI-tagging systems
- Design, maintain, and run QA processes to validate data across hundreds of thousands of vendors, proactively catching issues before they reach clients
- Investigate and resolve complex data quality issues independently
- Serve as a trusted resource for internal and external stakeholders by deeply understanding client use cases and aligning them with our data methodologies, coverage, and limitations
- Conduct and deliver detailed analytical write-ups on specific vendors, sectors, or trends that showcase the power of Signals data and drive client engagement
- Collaborate cross-functionally with Engineering, Product, and Go-to-Market to ship improvements fast
- Help shape the product roadmap by translating customer feedback and data quality insights into actionable improvements
- Proactively identify opportunities to improve efficiency, robustness, and data quality within team workflows and build AI automation where possible
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