Senior Databricks Engineer

📋 Description

• • This Senior Databricks Engineer role is critical to building scalable, cloud-native data platforms that power AI-driven enterprise solutions, enabling proactive compliance and sustainable automation for clients leveraging advanced data workflows.
• • The person will design and lead development of high-performance data engineering systems using Databricks, Apache Airflow, Spark, and Delta Lake, collaborating with ML teams on LLM/BERT-based workflows while ensuring data governance and architectural best practices.
• • Day-to-day responsibilities include architecting and implementing robust ETL/ELT pipelines, optimizing data workflows for performance and reliability, integrating GenAI tools to enhance developer efficiency and data quality, and mentoring junior engineers on platform standards.
• • The role involves close collaboration with data science and ML teams to support feature engineering and AI-driven data workflows, ensuring seamless data flow from ingestion to model training and inference.
• • The engineer will evaluate and implement modern data platform technologies, enforce data quality and lineage standards, and contribute to the evolution of the company’s lakehouse architecture on Azure and AWS Databricks environments.
• • They will participate in architectural reviews, define SLAs for data pipelines, and drive automation of monitoring, alerting, and self-service data capabilities across the organization.
• • The person will stay current with emerging trends in data engineering, GenAI, and lakehouse architectures, proposing innovations that improve scalability, cost-efficiency, and time-to-insight for business stakeholders.
• • About the team or company: Weekday 1 partners with innovative enterprises and startups like Amelia.ai to deliver AI-at-scale solutions, combining deep expertise in data engineering with cutting-edge AI technologies to solve complex business challenges.
• • The company fosters a fully remote, India-based work culture that values technical excellence, ownership, and continuous learning, offering engineers the autonomy to impact large-scale data platforms from anywhere in the country.
• • What the person can learn or achieve: Mastery of enterprise-grade Databricks lakehouse implementations, end-to-end ownership of AI-ready data platforms, and influence over architectural decisions that shape how enterprises adopt AI responsibly and at scale.
• • Opportunity to work with cutting-edge GenAI tools in data workflows, gain exposure to LLM/NLP integration patterns, and grow into a technical leadership role in data engineering within a fast-evolving AI landscape.