Data Engineer

About Us At You.com, we are building the AI Search Infrastructure that powers modern AI systems. Our goal is to create the trusted knowledge layer that agents, applications, and enterprises rely on to retrieve real-time, accurate, and citation-backed information. Our platform combines proprietary vertical indexes with LLM-optimized retrieval systems to power AI agents, applications, and enterprise workflows. We are solving hard problems across search, large language models, and large-scale infrastructure to make AI systems more reliable, transparent, and useful. Our team includes engineers, researchers, product builders, and operators who care about solving meaningful problems and delivering real-world impact. Whether you are improving core infrastructure, shaping product experiences, or helping bring new AI capabilities to market, your work will help define how modern AI finds and uses knowledge. About the Role We are looking for a hands-on Data Engineer to help build and scale our modern data platform. In this role, you will work closely with engineering, product, and analytics teams to develop reliable, high-performance data pipelines and systems. You’ll contribute to both batch and real-time data processing using technologies like Databricks, AWS, and Kafka, while helping ensure data quality, accessibility, and usability across the organization. You’ll play a key role in enabling data activation, ensuring that high-quality data flows not only into the warehouse but also outward to business tools like Salesforce, HubSpot, and Braze. Additionally, you will help power next-generation AI-driven applications , including agent-based systems and retrieval-augmented generation (RAG), by building robust data foundations and pipelines. This is a great opportunity for someone who enjoys solving data challenges end-to-end from ingestion to insights. Responsibilities Build and maintain scalable data pipelines (batch and streaming) using tools like Databricks, Spark, Kafka, and AWS services Design, develop, and optimize ETL/ELT workflows using DBT, PySpark, SQL , and tools like Fivetran Partner closely with marketing and growth teams to enable data use cases such as segmentation, campaign targeting, and lifecycle analytics Develop and maintain reverse ETL pipelines to sync data from the warehouse to tools like Salesforce, HubSpot, Braze , and other downstream systems Create and manage curated datasets to support analytics, reporting, and go-to-market initiatives Build and maintain dashboards and reporting layers to support marketing and business performance tracking Support AI/ML and agent-based applications by preparing and serving high-quality datasets for RAG pipelines and MCP (Model Context Protocol) integrations Monitor pipeline performance, troubleshoot issues, and ensure high data reliability and quality Implement data quality checks, validations, and alerting mechanisms across both ingestion and activation layers Collaborate with cross-functional teams to define data contracts and ensure consistency across systems Qualifications 6+ years of experience in data engineering or a related field Strong hands-on experience with Databricks, AWS (S3, Glue, Athena, EMR, etc.), and Kafka Proficiency in Python (PySpark) and SQL for large-scale data processing Experience building and maintaining ETL/ELT pipelines (DBT/Airflow or similar experience preferred) Experience with data ingestion tools such as Fivetran (or similar) Familiarity with reverse ETL / data activation workflows and syncing data to tools like Salesforce, HubSpot, Braze Exposure to or experience with AI/ML data pipelines , including RAG architectures, vector databases, or embeddings workflows Familiarity with agent-based systems, MCP integrations, or LLM-powered applications is a strong plus Experience working with marketing, Product or growth teams on data use cases (segmentation, attribution, campaign analytics, et ... (truncated, view full listing at source)