Meta - San Francisco, CA

posted about 2 months ago

Full-time
San Francisco, CA
Web Search Portals, Libraries, Archives, and Other Information Services

About the position

As a Data Engineer in Product Analytics at Meta, you will play a crucial role in shaping the data architecture for various large-scale projects. This position requires a collaborative approach, working closely with software engineering, data science, and product management teams to optimize growth strategies and enhance user experiences for over 3 billion users globally. Your contributions will directly impact company growth and user satisfaction, providing a unique opportunity to tackle some of the most complex data challenges in the industry with efficiency and integrity, at an unparalleled scale. In this role, you will conceptualize and own the data architecture, evaluating design and operational cost-benefit tradeoffs within systems. You will create and contribute to frameworks that enhance the efficacy of logging data, while also working with data infrastructure to triage issues and resolve them effectively. Collaboration is key, as you will engage with engineers, product managers, and data scientists to understand their data needs and represent key insights in a meaningful way. You will define and manage Service Level Agreements (SLAs) for all data sets within your areas of ownership, ensuring that security models are implemented based on privacy requirements. Addressing data quality issues and evolving governance processes will also be part of your responsibilities. Additionally, you will design, build, and launch sophisticated data models and visualizations that support various use cases across different products or domains. Your role will involve solving challenging data integration problems using optimal ETL patterns and frameworks, while also optimizing existing processes running in production. You will have the opportunity to mentor team members, providing and receiving actionable feedback to foster a collaborative and growth-oriented environment.

Responsibilities

  • Conceptualize and own the data architecture for multiple large-scale projects, evaluating design and operational cost-benefit tradeoffs.
  • Create and contribute to frameworks that improve the efficacy of logging data, while working with data infrastructure to triage issues and resolve them.
  • Collaborate with engineers, product managers, and data scientists to understand data needs and represent key data insights meaningfully.
  • Define and manage SLA for all data sets in allocated areas of ownership.
  • Determine and implement the security model based on privacy requirements, confirm safeguards are followed, and address data quality issues.
  • Design, build, and launch collections of sophisticated data models and visualizations that support multiple use cases across different products or domains.
  • Solve challenging data integration problems, utilizing optimal ETL patterns, frameworks, and query techniques, sourcing from structured and unstructured data sources.
  • Assist in owning existing processes running in production, optimizing complex code through advanced algorithmic concepts.
  • 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.
  • Mentor team members by giving and receiving actionable feedback.

Requirements

  • Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience.
  • 4+ years of work experience in data engineering (a minimum of 2+ years with a Ph.D).
  • Experience with SQL, ETL, data modeling, and at least one programming language (e.g., Python, C++, C#, Scala, etc.).

Nice-to-haves

  • Master's or Ph.D degree in a STEM field.
  • Experience with data processing automation, data quality, data warehousing, data governance, business intelligence, data visualization, data privacy.
  • Experience working with terabyte to petabyte scale data.
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