Senior Data Engineer

Now, more than ever, the Toast team is committed to our customers. We’re taking steps to help restaurants navigate these unprecedented times with technology, resources, and community. Our focus is on building the restaurant platform that helps restaurants adapt, take control, and get back to what they do best: building the businesses they love. And because our technology is purpose-built for restaurants, by restaurant people, restaurants can trust that we’ll deliver on their needs for today while investing in experiences that will power their restaurant of the future. 

Bready* to make a change?

Toast is building a one stop shop for restaurants to make it easier, faster, and more enjoyable to manage a successful restaurant. Toast is scaling rapidly and with that growth comes an exciting new opportunity to join our new organization in Bangalore. Toast is looking for a Senior Software Engineer (Data Engineer) to embed directly within the Data Science team where you'll play a crucial role in bridging the gap between data availability and model readiness. Your mission is to transform raw and warehouse data into consumable, production-grade data pipelines and data artifacts that power machine learning and statistical models to scale Toast AI platform and build highly impactful Fintech AI products. 

You’ll partner closely with data scientists, analysts, and ML engineers to build performant and reliable pipelines that enable experimentation, prototyping, and production-scale deployment.

You will enable restaurants to access data-driven meaningful insights, enable them to take impactful actions by leading the team to develop the best-in-class solution. 

About this Roll*:

• Design, build, and maintain end-to-end data pipelines supporting machine learning workflows — including raw data ingestion, feature engineering, model training datasets, and inference-ready outputs.
• Partner closely with data scientists, ML engineers, product managers, and software engineers to translate business needs into scalable data solutions.
• Build and enhance our data platform and enterprise data warehouse, enabling consistent, reliable access to data as the business scales.
• Develop and optimize ETL/ELT pipelines for key datasets such as restaurant orders, payments, and pricing, ensuring high-quality, timely data is available for modeling and analysis.
• Support the operationalization of ML pipelines, contributing to the design and deployment of feature stores, retraining loops, and serving pipelines.
• Engage in data model design and optimization — including new Hive table creation and tuning of existing schemas based on usage patterns and performance needs.
• Balance tactical data requests (e.g., building new data assets) with strategic initiatives such as improving platform reliability, reusability, and scalability.
• Contribute to the evolution of CI/CD pipelines and deployment practices to improve developer velocity and autonomy within the data science and analytics ecosystem.

Work Mode: This role follows a hybrid work model, requiring a minimum of 2 days per week in the office.

Do you have the right ingredients*?

• Bachelor’s degree or equivalent practical experience
• 5+ years of experience as a Software or Data Engineer, with a proven track record of delivering data-driven solutions at scale.
• Strong data modeling expertise, with the ability to understand business use cases and design robust pipelines that support both current analytical needs and future scalability.
• Deep hands-on experience in distributed data processing and streaming frameworks, particularly using Apache Flink (stateful computations) and Amazon Kinesis for real-time pipelines, as well as batch processing with tools like Apache Spark.
• Proficiency in PySpark, Scala, and SQL for large-scale data transformations and processing.
• Familiarity with data serialization and compression formats, including Avro and Protobuf schemas, and integration tools such as Kafka and Snowflake connectors.
• Experience building and optimizing Feature Stores to support high-throughput online model inference and efficient offline model retraining workflows.
• Practical experience working with cloud-native infrastructure, especially within the AWS ecosystem (e.g., SageMaker, DynamoDB, Athena, Lake Formation).
• Strong programming skills in object-oriented languages such as Python, Java, Kotlin, or Scala. (We believe great engineers can adapt to new languages.)
• Demonstrated ability to design and implement scalable, production-grade data pipelines, with attention to data quality, monitoring, and performance optimization.
• A solid understanding of distributed systems, software engineering principles, and event-driven architectures, with experience building fault-tolerant, secure, and highly available applications.
• A collaborative mindset and experience working closely with product teams to design solutions that create value for both customers and the business.
• A strong sense of ownership, empathy, and respect for team collaboration and end-user experience.
• A passion for building well-engineered, usable, and accessible systems that stand the test of scale and change.
• Experience working in an Agile/Scrum environment and a willingness to adapt to evolving priorities.

Bonus ingredients*:

• Experience with workflow orchestration tools like Apache Airflow, MLflow, or infrastructure-as-code tools like Terraform.
• Familiarity with machine learning deployment workflows, including versioning, CI/CD for models, and monitoring.
• Foundational knowledge of statistical methods (e.g., regression, classification) and deep learning algorithms (e.g., CNNs, RNNs).
• Understanding of enterprise integration patterns, including message queues, pub/sub systems, and event-driven design.