Data Engineer, Analytics (Technical Leadership)

Meta•San Francisco, CA

4d•$253,000 - $314,000

About The Position

Every month, billions of people leverage Meta products to connect with friends and loved ones from across the world. On the Data Engineering Team, our mission is to support these products both internally and externally by delivering the best data foundation that drives impact through informed decision making. As a highly collaborative organization, our data engineers work cross-functionally with software engineering, data science, and product management to optimize growth, strategy, and experience for our 3 billion plus users, as well as our internal employee community. We are looking for a technical leader in our Data Engineering team to work closely with Product Managers, Data Scientists and Software Engineers to support building out a great platform for the future of computing. In this role, you will observe a direct correlation between your work, company growth, and user satisfaction. You’ll collaborate with experienced engineers, data scientists, and product managers, work with large-scale, diverse data sets, use modern data engineering tools and frameworks, and experience your efforts affecting products and people on a regular basis.

Requirements

Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
12+ years of experience in the data warehouse space
12+ years of experience in custom ETL design, implementation, and maintenance
12+ years of experience with object-oriented programming languages
12+ years of experience with schema design and dimensional data modeling
12+ years of experience in authoring SQL statements
Experience analyzing data to identify deliverables, gaps and inconsistencies
Experience managing and communicating data warehouse plans to internal clients

Nice To Haves

Knowledge and practical application of Python
Experience influencing product decisions with data
BS/BA in Technical Field, Computer Science or Mathematics
Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
Experience working with either a MapReduce or an MPP system
Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
Experience driving projects independently across distributed or global teams, coordinating across time zones and stakeholders

Responsibilities

Proactively drive the vision for data foundation and analytics to accelerate building and improvement of cross platform components across Instagram, and define and execute on plan to achieve that vision
Conceptualize and own the data architecture for multiple large-scale projects, while evaluating design and operational cost-benefit tradeoffs within systems
Create and contribute to frameworks that improve the efficacy of logging data, while working with data infrastructure to triage issues and resolve
Build cross-functional relationships with Data Scientists, Product Managers and Software Engineers to understand data needs and deliver on those needs
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, address data quality issues, and evolve governance processes within allocated areas of ownership
Design, build, and launch collections of sophisticated data models and visualizations that support use cases across different products or domains
Solve our most challenging data integrations problems, utilizing optimal ETL patterns, frameworks, 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 providing actionable feedback and fostering a culture of continuous improvement