Job Description
The SpringCube team curated the following job opportunity to help you in your job search. Explore the position below to find your next career move.
Company Overview
A leading global creative technology organization is transforming the future of professional design through intelligent, connected products that combine creativity, collaboration, and artificial intelligence. Its Pro Design organization develops industry-leading creative applications and emerging experiences designed to help creators and teams work faster while maintaining high standards of quality and craftsmanship.
The organization is seeking a Senior Data Engineer to build a strong, scalable data foundation supporting next-generation design products. The role will focus on developing reliable data architecture, production-grade pipelines, data governance, analytics infrastructure, and AI-driven data capabilities across a broad portfolio of professional design experiences.
The successful candidate will work closely with Product, Engineering, and Data Science teams to transform complex and ambiguous business and product questions into reliable data models and scalable solutions. The position requires strong expertise in data engineering, cloud technologies, Databricks, AI-enabled workflows, and high-volume product telemetry.
Key Responsibilities
- Build, scale, and optimize data architecture and ETL pipelines using Databricks across professional design products.
- Develop data solutions spanning shared platform data and in-application usage telemetry.
- Partner with Product, Engineering, and Data Science teams to define and implement data logging and instrumentation.
- Develop data foundations that support product experimentation, AI/ML features, analytics, and business intelligence.
- Establish and continuously improve data governance standards covering data quality, privacy, security, lineage, and service-level agreements.
- Utilize Unity Catalog or comparable governance technologies to maintain reliable and secure data environments.
- Build automated reporting capabilities and data foundations that support conversational and AI-driven insights.
- Maintain the health, reliability, and performance of production pipelines and queries at scale.
- Drive technical optimization initiatives focused on improving cost efficiency and reducing latency.
- Integrate AI tools into data engineering workflows to improve efficiency, quality, and performance.
- Collaborate across functions to develop scalable solutions for evolving product and business requirements.
Required Qualifications
- Bachelor’s degree in Computer Science, Computer Engineering, a relevant technical field, or equivalent practical experience.
- Strong proficiency in SQL and Python.
- Hands-on experience with PySpark, Airflow, and Databricks workflows.
- 5+ years of professional experience in data engineering, including experience managing production pipelines at scale.
- Experience working with high-volume event and telemetry data generated by client applications.
- Experience integrating AI tools to redesign or optimize workflows for improved efficiency, quality, or latency.
- Experience building multi-agent AI systems or agentic workflows.
- Working knowledge of AI/ML data pipelines, including feature stores, embeddings, or retrieval systems.
- Familiarity with modern business intelligence and reporting tools.
- Proven ability to partner with Product, Engineering, and Data Science teams to translate ambiguous requirements into reliable data models and solutions.
Preferred Qualifications
- Master’s degree or Ph.D. in Computer Science, Computer Engineering, a relevant technical field, or equivalent practical experience.
- Hands-on experience with Databricks and Unity Catalog or comparable Lakehouse and data governance technologies.
- Experience deploying and managing services and applications on Microsoft Azure and Azure cloud platforms.
- Familiarity with Retrieval-Augmented Generation (RAG) techniques and their use with Large Language Models.
- Knowledge of data privacy and compliance requirements for user-behavior data, including GDPR and CCPA.
- Experience working with AI/ML infrastructure and modern data platforms.
- Strong understanding of scalable data architecture, data governance, and production data engineering practices.
Disclaimer
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