Staff Data Engineer

Job Description Roles & Responsibilities About the Role Are you passionate about building modern, scalable data platforms that power business decisions and product innovation? As a Staff Data Engineer at Toters, you'll define the technical direction of our data platform while designing and building reliable, high-performance data infrastructure that scales with our business. You'll lead the architecture of our batch and streaming data ecosystem, establish engineering standards, and drive platform evolution across multiple teams. Working closely with Engineering Managers, Software Engineers, Product Managers, Analytics, and Machine Learning teams, you'll influence technical strategy, mentor engineers, and ensure our data platform remains scalable, reliable, and cost-efficient. This is a highly hands-on technical leadership role where you'll shape how data is ingested, processed, governed, and consumed across the organization. In this role, you will: Define and drive the long-term technical vision and architecture for Toters' data platform. Lead the design and implementation of scalable batch and streaming data platforms that support analytics, operational systems, and machine learning. Design resilient, cloud-native data architectures capable of processing high-volume event streams and transactional workloads. Establish engineering standards, architectural patterns, and best practices for data engineering across the organization. Design scalable data models, schemas, and data contracts that enable long-term maintainability, governance, and interoperability. Drive architectural decisions that improve scalability, reliability, security, observability, and operational efficiency across the platform. Lead complex technical initiatives such as platform migrations, streaming modernization, lakehouse evolution, and data infrastructure improvements. Evaluate new technologies and recommend pragmatic solutions that balance scalability, operational complexity, and cost. Improve platform observability through monitoring, alerting, data quality validation, lineage, and operational dashboards. Own the reliability of critical production data systems by leading incident response, root cause analysis, and long-term platform improvements. Optimize cloud infrastructure, storage, compute, and streaming costs while maintaining high platform performance. Partner with Engineering Managers, Product Managers, Analytics, Machine Learning, and Software Engineering teams to define platform capabilities and technical roadmaps. Mentor Senior Data Engineers through architecture reviews, technical coaching, and engineering leadership. Conduct high-quality code and design reviews that raise the technical bar across multiple teams. Drive technical discussions, resolve complex engineering challenges, and influence cross-functional decision-making across the engineering organization. Why Toters? Flexible work environment with hybrid-friendly roles. Opportunity to define and shape the future of Toters' modern data platform. Lead high-impact technical initiatives that influence multiple engineering teams. Solve complex engineering challenges involving distributed systems, streaming, and large-scale data processing. Collaborate with talented engineers in a culture of mentorship, ownership, and continuous learning. Direct impact on products used by thousands of customers every day. Competitive compensation package. Exclusive discounts on Toters orders. First-class medical insurance. Desired Candidate Profile Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field. 9+ years of experience designing and building large-scale production data platforms or distributed data systems. Deep expertise in SQL and advanced data modeling, including dimensional modeling (facts and dimensions), schema design, and data architecture. Expert-level experience building distributed streaming systems using Apache Kafka, Amazon Kinesis, Apache Pulsar, or equivalent technologies, with strong knowledge of partitioning, consumer groups, delivery semantics, schema management, and production troubleshooting. Deep understanding of distributed systems, batch and streaming data processing, event time, processing time, windowing, stateful processing, fault tolerance, and the challenges of achieving exactly-once processing. Strong programming skills in Python and/or JVM languages such as Java or Scala. Extensive experience designing modern data lakehouse architectures using Amazon S3, Parquet, and open table formats such as Apache Iceberg, Delta Lake, or Apache Hudi. Experience building cloud-native data platforms on AWS, Google Cloud Platform (GCP), or Microsoft Azure, including object storage, streaming services, IAM, networking, infrastructure security, and cloud cost optimization. Hands-on experience with distributed processing technologies such as Apache Flink, ClickHouse, Trino, and similar analytical platforms. Experience designing and implementing Change Data Capture (CDC) architectures using Debezium or similar technologies. Experience with modern transformation frameworks such as dbt. Strong experience with Infrastructure as Code using Terraform, containerization using Docker, and orchestration platforms such as Kubernetes. Proven experience owning mission-critical production systems, leading incident response, and driving long-term platform reliability. Demonstrated ability to define engineering standards, influence architectural decisions, and lead technical initiatives across multiple teams. Excellent communication and technical writing skills, with the ability to document architecture, influence stakeholders, and mentor senior engineers. Passion for building scalable, reliable, maintainable, and cost-efficient data platforms. Nice to Have Experience designing enterprise-scale streaming architectures using Apache Flink. Experience leading lakehouse migrations using Apache Iceberg or similar technologies. Experience implementing large-scale CDC platforms using Debezium. Experience building self-service data platforms and developer tooling. Experience with ML Feature Stores, Customer Data Platforms (CDPs), or real-time personalization platforms. Experience working in high-growth technology companies, marketplace platforms, or on-demand delivery businesses. Experience contributing to open-source technologies or internal engineering frameworks. Company Industry InternetE-commerceDotcom Department / Functional Area IT Software Keywords Staff Data Engineer Get real-time job updates only on our App