Data Engineer, Meta Superintelligence Labs (Mobile Client)

We are looking for a Data Engineer to join our PAR organization where your technical skills and analytical mindset will be utilized designing and building some of the world's most extensive data sets, helping to craft experiences for billions of people and hundreds of millions of businesses worldwide.

In this role, you will collaborate with software engineering, data science, and product management teams to design/build scalable data solutions across Meta to optimize growth, strategy, and user experience.

You will own the client-side logging and measurement foundation for a consumer mobile application on iOS and Android. You will design and implement product and performance instrumentation directly in mobile codebases, and build the pipelines and metrics that allow product, engineering, and data science partners to measure engagement, funnels, latency, and product quality end to end. The instrumentation you own is the basis for the team's primary decision-making surfaces, so you will be accountable for the accuracy and reliability of measurement across the product.

You will help shift client instrumentation from a manual, per-feature effort to a scalable capability - defining logging standards for each client surface, building tooling and automated validation, and enabling partner teams to implement high-quality instrumentation themselves as product velocity increases.

You will be at the forefront of identifying and solving some of the most interesting data challenges at a scale few companies can match. By joining Meta, you will become part of a world-class data engineering community dedicated to skill development and career growth in data engineering and beyond.

Data Engineering: You will guide teams by building optimal data artifacts (including datasets and visualizations) to address key questions. You will refine our systems, design logging solutions, and create scalable data models. Ensuring data security and quality, and with a strong focus on efficiency, you will suggest architecture and development approaches and data management standards to address complex analytical problems.

Product leadership: You will use data to shape product development, identify new opportunities, and tackle upcoming challenges. You'll ensure our products add value for users and businesses, by prioritizing projects, and driving innovative solutions to respond to challenges or opportunities.

Communication and influence: You won't simply present data, but tell data-driven stories. You will convince and influence your partners using clear insights and recommendations. You will build credibility through structure and clarity, and be a trusted strategic partner.

Responsibilities

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 Collaborate with engineers, product managers, and data scientists to understand data needs, representing key data insights visually in a meaningful way Define and manage Service Level Agreements 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 multiple use cases across different products or domains Solve our most challenging data integration problems, utilizing optimal Extract, Transform, Load (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 Design, implement, debug, and validate client-side product and performance instrumentation in production iOS and Android codebases Establish and document client logging standards, and build automated validation and testing to detect instrumentation gaps and regressions before launch Build tooling that enables engineering and cross-functional partners to implement high-quality instrumentation independently, scaling measurement capacity beyond direct headcount Partner with mobile engineering teams to build the foundational hooks and frameworks that make client instrumentation testable and maintainable across platforms Influence product and cross-functional teams to identify data opportunities to drive impact Mentor team members by giving/receiving actionable feedback

Qualifications

Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience 7+ years of experience where the primary responsibility involves working with data. This could include roles such as data analyst, data scientist, data engineer, or similar positions 7+ years of experience with SQL, ETL, data modeling, and at least one programming language (e.g., Python, C++, C#, Scala or others.) Experience designing and building batch data pipelines and data foundations using a workflow orchestration framework and a distributed SQL query engine Hands-on experience implementing, debugging, and validating client-side logging and instrumentation in production mobile codebases (iOS and Android), including authoring and landing the instrumentation code Experience defining and implementing product measurement for consumer-facing products, including engagement, retention, funnel, and latency metrics Master's or Ph.D degree in a STEM field Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies Demonstrated experience prioritizing instrumentation and measurement investments on fast-moving consumer surfaces with constrained engineering capacity Demonstrated experience establishing measurement standards and driving adoption across multiple engineering teams and client platforms in a cross-functional environment