Data Engineer IV (6281)

itD Tech•San Jose, CA

7h•Remote

About The Position

itD is seeking a Data Engineer IV to build scalable data infrastructure and AI-enabled analytics capabilities that support customer experience initiatives across Company Reality Labs' Post Sales Organization (RL PSO). The ideal candidate will bring deep experience in data engineering, scalable data pipelines, data architecture, analytics, and AI/ML enablement, with a track record of delivering reliable data platforms, self-service dashboards, and automation solutions that improve business insights and operational efficiency. Location: Remote – US, with preference for candidates in the PST timezone Duration: 12 months, with a strong possibility of extension Schedule: Full-time, 40 hours/week, Monday–Friday; no overtime Start Date: ASAP We provide comprehensive medical benefits, a 401k plan, paid holidays, and more. Please note that we are only considering direct W2 candidates at this time, as we are unable to offer sponsorship.

Requirements

6+ years of overall professional experience in data engineering or a closely related field; candidates with prior Meta experience may qualify with less experience.
Bachelor's degree in Computer Science or a related field.
Strong proficiency in SQL and Python.
Proven experience designing and building production-grade ETL/ELT pipelines at scale.
Experience with Airflow or an equivalent workflow orchestration platform.
Experience with Apache Spark or comparable large-scale data processing technologies.
Experience building self-service dashboards using Tableau or an equivalent data visualization platform.
Experience with data warehousing platforms such as Hive, Presto, Spark, Snowflake, or BigQuery.
Experience with data quality, data governance, and data modeling best practices.
Experience manipulating large datasets to generate insights and support business decisions.
Demonstrated ability to operate independently, manage ambiguity, and deliver results in a fast-paced environment.
Strong communication skills and experience working with both technical and non-technical stakeholders.
Experience collaborating cross-functionally and influencing business decisions through data-driven insights.

Nice To Haves

Experience with generative AI, LLMs, prompt engineering, RAG architectures, LLM APIs, or AI agent workflows.
Prior Meta experience and familiarity with large-scale technology infrastructure.
Experience with Git, CI/CD for data pipelines, and infrastructure-as-code practices.
Experience with streaming technologies such as Kafka or Spark Streaming.
Knowledge of metadata management, data cataloging, and data lineage.
Experience with customer experience/customer support operations and related metrics.
Familiarity with customer support platforms and digital analytics tools.
Experience with Python or Bash scripting for automation and internal tooling.
Familiarity with Agile development methodologies.
Experience in high-volume consumer electronics or other fast-paced technology environments.
Prior experience working as a contingent worker or contractor in a fast-paced organization.

Responsibilities

Design, develop, and maintain scalable batch and streaming data pipelines, ETL/ELT workflows, data models, and data warehouse architectures supporting multiple customer experience use cases.
Build and optimize data orchestration workflows, ensuring reliable scheduling, dependency management, pipeline performance, and efficient use of large-scale data infrastructure.
Implement data quality frameworks, including validation, monitoring, alerting, and data lineage, to maintain the accuracy and reliability of business-critical data assets.
Develop interactive self-service dashboards and visualizations that integrate customer interactions, feedback, behavioral data, and operational information to identify trends and performance opportunities.
Enable AI/ML-powered analytics by developing feature pipelines, curated datasets, model-ready data assets, and AI-assisted reporting and automation capabilities.
Leverage large language models and generative AI technologies to automate data workflows, accelerate insight generation, and improve stakeholder self-service capabilities.
Collaborate with customer service, operations, product, engineering, analytics, and data science teams to translate business requirements into scalable data solutions and drive customer experience improvements.
Attend regular internal practice community meetings.
Collaborate with your itD practice team on industry thought leadership.
Complete client case studies and learning material (blogs, media material).
Build out material to contribute to the Digital Transformation practice.
Attend internal itD networking events (in person and virtual).
Work with leadership on career fast-track opportunities.