Senior Data Engineer (Databricks)

Requirements

• 5+ years of professional experience shipping high-quality, production-ready code.
• Strong computer science foundations, including data structures & algorithms, OS, computer networks, databases, and object-oriented programming.
• Experience with Python.
• Experience in setting up data pipelines using relational SQL and NoSQL databases , including Postgres, Cassandra or MongoDB.
• Strong experience with data processing and warehousing tools such as Databricks and Snowflake .
• Experience with cloud platforms such as AWS, GCP, or Azure
• Experience with processing and orchestration, such as Spark or Airflow .
• Proven success in manipulating, processing, and extracting value from large datasets.
• Expertise with version control systems, such as Git.
• Strong analytic skills related to working with unstructured datasets.
• Excellent verbal and written communication skills in English .
• (Nice to have) Familiarity with graph technologies (e.g., Neo4j) or graph-based data modeling .

Benefits

Additional Information

Fully remote | Complete engagement job Founded in Palo Alto by Dr. Andrew Ng and Israel Niezen, Factored helps U.S. companies build and scale world-class AI, ML, and Data teams, powered by the top 1% of LATAM talent, with a defining purpose: To empower brilliant humans, unleash their potential, and amplify their impact in the world. At Factored, you'll be part of a community that values learning, ownership, and authenticity, where your growth is personal and your ideas matter. We're transparent, curious, and collaborative. We strive for excellence, celebrate diversity, encourage curiosity, and build an environment where you can truly thrive. As a Senior Data Engineer, you will engage full-time in designing and evolving the data architectures that power intelligent products for global and Fortune 500 organizations. You'll build high-performance pipelines, optimize large-scale processing frameworks, and design data models that enable analytics, machine learning, and enterprise-grade decision-making. This role is ideal for engineers who excel at navigating complex data environments, think in terms of scalable systems, and take ownership of data reliability, quality, and performance across the entire lifecycle. Your work will ensure teams can experiment faster, deliver smarter products, and unlock meaningful insights through trusted, well-orchestrated data. We are especially interested in candidates with strong hands-on experience in Databricks and Snowflake , and exposure to graph-based data models or technologies is a strong plus . Functional Responsibilities: Develop and maintain ETL (Extract, Transform, Load) processes using Python . Write efficient SQL queries to extract, manipulate, and analyze data from various databases. Design, build, and optimize large-scale data pipelines using modern cloud data platforms such as Databricks and Snowflake. Design and develop optimal data processing techniques, including automating manual processes, data delivery, data validation, and data augmentation. Collaborate with stakeholders to understand data needs and translate them into scalable solutions. Design and develop API integrations in order to feed different data models. Architect and implement new features from scratch, partnering with AI/ML engineers to identify data sources, gaps and dependencies. Identify bugs and performance issues across the stack, including performance monitoring and testing tools to ensure data integrity and quality user experience. Build a highly scalable infrastructure using SQL and AWS big data technologies. Keep data secure and compliant with international data handling rules. (Nice to have) Work with or contribute to graph data models or graph databases (e.g., Neo4j) to support relationship-driven use cases.

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