Competitive Data Eng & Analyst
ebayinc
Job Description
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Build and Maintain Data Pipelines
- Build, maintain, and improve data pipelines to ingest, transform, and integrate third-party/vendor datasets into the data warehouse.
- Develop and support ETL/ELT workflows to improve data quality, reliability, and timeliness.
- Partner with Data Engineering teams to implement data models, optimize queries, and improve data processing efficiency.
- Create and maintain data schemas, validation checks, and monitoring mechanisms to improve data integrity.
- Automate recurring ingestion, transformation, and reporting workflows to reduce manual effort.
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Support Data Integration and Vendor Data Operations
- Support onboarding of external and vendor data sources, including requirements gathering, data validation, and pipeline implementation.
- Work with internal stakeholders and vendors to investigate data issues and improve data consistency.
- Help standardize disparate data sources into reusable and scalable datasets.
- Document data sources, business logic, transformation rules, and known limitations to improve transparency and reusability.
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Build Reporting and Visualization Solutions
- Develop dashboards and reporting layers using Tableau or similar BI tools.
- Create reusable datasets, data marts, and semantic layers to support self-serve analytics.
- Improve dashboard performance, usability, and maintainability through optimization and standardization.
- Translate business questions into structured analyses, dashboards, and scalable reporting solutions.
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Apply AI and Automation
- Use AI-enabled tools, LLMs, and automation frameworks to improve data processing, analysis, and reporting workflows.
- Build automated workflows to support competitive intelligence generation, recurring analysis, and insight delivery.
- Identify practical opportunities to reduce manual effort and improve speed, consistency, and scalability.
- Support use cases involving AI-assisted analytics, summarization, classification, and workflow automation.
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Partner with Stakeholders
- Collaborate with cross-functional partners across strategy, analytics, product, data engineering, and business teams.
- Communicate technical approaches, data limitations, timelines, and trade-offs clearly to both technical and non-technical audiences.
- Support long-term roadmap initiatives as well as fast-turn analytical and operational requests.
- Work effectively with global teams across time zones.
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Contribute to Operational Excellence
- Identify data quality, scalability, and process bottlenecks and help implement practical solutions.
- Contribute to best practices for data governance, pipeline reliability, documentation, and analytics workflows.
- Support continuous improvement of competitive intelligence data infrastructure and reporting processes.
- Provide input on feasibility, timelines, and implementation approaches.