Duration:12+ months
Summary:
Data Engineering & Architecture — 40%
- Design, develop, integrate, launch, and maintain scalable data pipelines (batch and streaming) that support multiple use cases across PSO
- Build and optimize ETL/ELT workflows, data models, and data warehouse architectures to facilitate efficient development of data artifacts
- Implement data quality frameworks including validation, monitoring, alerting, and lineage tracking to ensure data reliability
- Develop and manage orchestration workflows (e.g., Airflow, Dataswarm) for scheduling and dependency management of data pipelines
- Optimize query performance, pipeline efficiency, and storage costs across large-scale data infrastructure
- Analytics & Visualization — 20%
- Create interactive and dynamic data visualizations that communicate complex insights to stakeholders
- Work with various data sources—including customer interactions, feedback, behavioral data, and operational logs—to integrate and build reports that identify pain points and trends
- Develop and track key performance indicators to measure the effectiveness of customer experience initiatives
- AI Enablement & Automation — 40%
- Enable AI/ML-powered analytics by building and maintaining feature pipelines, curated datasets, and model-ready data assets
- Leverage large language models (LLMs) and generative AI tools to automate data workflows, accelerate insight generation, and enhance self-service capabilities for stakeholders
- Develop and maintain prompt engineering frameworks, AI-assisted reporting tools, and intelligent automation solutions that scale team productivity
- Partner with engineering and data science teams to integrate AI/ML model outputs into dashboards and operational workflows
- Evaluate and implement emerging AI tools and techniques to continuously improve the team's analytics and data engineering capabilities
- Cross-Functional Partnership
- Work closely with customer service and operations, product, and engineering teams to integrate data insights into business decisions and drive customer experience improvements
- Champion data literacy and AI enablement across the organization through documentation, training, and best practice sharing
Minimum Qualifications
- 8 years of experience doing quantitative and operational analyses in a customer support/service, e-commerce, or order management organization
- Strong data engineering skills: experience designing and building production-grade data pipelines, data models, and ETL/ELT processes at scale
- Proficiency in SQL (complex queries, performance tuning, window functions) and at least one programming language (Python preferred)
- Experience with data warehousing platforms (e.g., Hive, Presto, Spark, Snowflake, or BigQuery)
- Experience with workflow orchestration tools (e.g., Airflow, Dataswarm, or equivalent)
- Experience with data visualization tools such as Tableau, Looker, or equivalent for creating self-service dashboards
- Demonstrated experience with data quality frameworks, data governance, and data modeling best practices (dimensional modeling, star/snowflake schemas)
- Hands-on experience with AI/ML enablement—such as building feature pipelines, working with LLM-based tools, or implementing AI-assisted analytics workflows
- Analytics experience manipulating large datasets to formulate insights and drive solutions
- Track record of operating independently, managing ambiguity, and delivering results
- Strong communication skills with experience articulating issues to both technical and non-technical audiences
- Cross-functional experience, including leading or influencing change through data-driven insights
Preferred Qualifications
- Experience with generative AI tools and frameworks (e.g., prompt engineering, RAG architectures, LLM APIs, or AI agent workflows)
- Familiarity with version control (Git), CI/CD for data pipelines, and infrastructure-as-code practices
- Experience with streaming data technologies (e.g., Kafka, Spark Streaming)
- Knowledge of metadata management, data cataloging, and data lineage tools
- Background and knowledge of CX/CS operations and metrics
- Familiarity with customer support platforms (e.g., Salesforce)
- Experience with digital analytics tools (e.g., Google Analytics, Adobe Analytics)
- Experience with scripting for automation (Python, Bash) and building internal tooling
- Familiarity with agile development methodologies
- Experience working in the high-volume consumer electronics industry
- Experience working with operations functions, preferably in the customer experience or customer support operations space
Surrounding team & key projects
This role sits within the Reality Labs Post Sales Organization (RL PSO), which handles everything after the point of sale—including customer experience and support. The team is responsible for PSO-wide data reporting and analytics, serving as the data backbone for three key pillars: Customer Support (covering emails, chats, and Client interactions with analytics sourced from Salesforce, BPO partners, and case management systems), Customer Experience Surveys (encompassing purchase, returns, store, and post-support surveys), and Digital Support (including help center articles, video articles, website issue creation, and chatbot analytics). The hiring manager, reports directly to the head of CX within Reality Labs PSO. Critically, this team does not perform the analysis itself—rather, it enables analysts across the organization by building robust data pipelines, creating and maintaining Hive tables, developing dashboards, and driving AI readiness across the PSO org.
Typical Day-to-Day in the role
- Building and maintaining self-service dashboards for stakeholders across PSO
- Enabling AI capabilities and integration across the organization to reduce manual effort
- Performing QA and validation for dashboards to ensure data accuracy and reliability
- Designing, building, and maintaining data pipelines to move data between systems
- Ingesting data from third-party sources and sharing data to external partners via APIs
- Collaborating with analysts and stakeholders to understand data requirements and deliver solutions
How will performance be measured?
- Quality, reliability, and uptime of data pipelines and dashboards
- Successful delivery and adoption of AI enablement initiatives across the organization
- Data accuracy and adherence to data quality standards and governance policies
- Timeliness in delivering data artifacts and meeting project deadlines
- Stakeholder satisfaction and effectiveness of self-service analytics capabilities
- Reduction in manual effort through automation and AI-driven workflows
- Ability to operate independently, manage ambiguity, and deliver results in a fast-paced environment
What makes this role interesting?
This is an opportunity to join Meta's Reality Labs at the forefront of AR/VR technology and directly impact the customer experience for millions of users worldwide. The role offers a unique blend of data engineering and AI enablement work—building the foundational data infrastructure while also driving cutting-edge AI capabilities across the organization. You will work in a fast-paced industry and company where your contributions will be highly visible and impactful. As a direct partner to the hiring manager, you will have significant ownership and influence over the team's data strategy and AI roadmap. There is a strong possibility of extension with full tenure for high performers, making this an excellent opportunity for candidates who thrive in the contractor model and are looking for stability and meaningful long-term work. The team values committed professionals who understand and prefer the contingent worker model and are looking for more than just a stopgap role.
Must-Have Skills
- SQL and Python proficiency with strong experience building ETL pipelines at scale
- Airflow or equivalent workflow orchestration tool experience (Apache Spark orchestration)
- Dashboard building experience (Tableau preferred)
Nice-to-have Skills
- AI experience (e.g., using Claude, AI-generative dashboards, enabling AI capabilities in other organizations)
- Prior Meta experience (understands internal infrastructure and tech stack)
- Experience in a fast-paced industry or company (startups acceptable; avoid candidates from slower-paced finance or healthcare environments)
Years of overall experience required?
6 (Could be less if the candidate has prior meta experience)
Degrees/certifications required?
Bachelor's degree in Computer Science or related field
Candidate Disqualifiers
- Candidates using this role as a stopgap or stepping stone to full-time employment
- Candidates who cannot adapt to Meta's fast-paced work culture
- Candidates without prior contingent worker/contractor experience (strongly prefer CW background over FTE candidates)
- Candidates from slower-paced industries who may struggle with the velocity of work
About US Tech Solutions: US Tech Solutions is a global staff augmentation firm providing a wide range of talent on-demand and total workforce solutions. To know more about US Tech Solutions, please visit www.ustechsolutions.com.
US Tech Solutions is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, colour, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.
AI Statement: By applying, you acknowledge that AI-assisted tools may be used during hiring.
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