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Data Engineer – Analytics
| Company | Meta |
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| Location | New York, NY, USA |
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| Salary | $254003 – $279400 |
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| Type | Full-Time |
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| Degrees | Bachelor’s |
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| Experience Level | Senior |
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Requirements
- Requires a Bachelor’s degree(or foreign degree equivalent) in Computer Science, Engineering, Information Systems, Mathematics, Statistics, Data Analytics, Applied Sciences, or a related field and 60 months of experience in the job offered or in a related occupation.
- Requires 60 months of experience involving each of the following:
- 1. Features, design, and use-case scenarios across a big data ecosystem
- 2. Custom ETL design, implementation, and maintenance
- 3. Object-oriented programming languages
- 4. Schema design and dimensional data modeling
- 5. Writing SQL statements
- 6. Analyzing data to identify deliverables, gaps, and inconsistencies
- 7. Managing and communicating data warehouse plans to internal clients
- 8. MapReduce or MPP system
- 9. Python
Responsibilities
- Design, model, and implement data warehousing activities to deliver the data foundation that drives impact through informed decision making.
- Design, build and launch collections of sophisticated data models and visualizations that support multiple use cases across different products or domains.
- Collaborate with engineers, product managers and data scientists to understand data needs, representing key data insights visually in a meaningful way.
- Define and manage SLA for all data sets in allocated areas of ownership.
- Create and contribute to frameworks that improve the efficacy of logging data, while working with data infrastructure to triage issues and resolve.
- Determine and implement the security model based on privacy requirements, confirm safeguards are followed, address data quality issues, and evolve governance processes within allocated areas of ownership.
- Solve challenging data integration problems utilizing optimal ETL patterns, frameworks, query techniques, and sourcing from structured and unstructured data sources.
- Optimize pipelines, dashboards, frameworks, and systems to facilitate easier development of data artifacts.
- Influence product and cross-functional teams to identify data opportunities to drive impact.
- Work on problems of diverse scope where analysis of data requires evaluation of identifiable factors.
- Demonstrate good judgment in selecting methods and techniques for obtaining solutions.
Preferred Qualifications
No preferred qualifications provided.
