Senior Data Engineer

Job Description Roles & Responsibilities About the Role Are you passionate about building scalable, reliable data platforms that power business decisions and customer experiences? As a Senior II Data Engineer at Toters, you'll design and build modern data infrastructure that enables reliable, real-time and batch data processing across the organization. You'll own the design and delivery of complex data pipelines, influence technical decisions within your domain, and help establish engineering best practices that improve the scalability, reliability, and maintainability of our data platform. Working closely with Software Engineers, Product Managers, Analytics, and Machine Learning teams, you'll help transform high-volume operational data into trusted, high-quality data products that drive business impact. In this role, you will: Collaborate with cross-functional teams to understand business requirements and translate them into scalable data solutions. Design, build, and maintain reliable batch and streaming data pipelines that support analytics, operational systems, and machine learning workloads. Develop scalable data ingestion frameworks capable of processing high-volume event and transactional data. Design and optimize data models that enable efficient analytics while supporting long-term maintainability. Build resilient streaming solutions while balancing throughput, latency, reliability, and operational cost. Develop and maintain modern data lakehouse architectures using cloud-native technologies and distributed data platforms. Design schemas and data contracts that promote consistency, governance, and interoperability across systems. Improve platform observability by implementing monitoring, alerting, data quality validation, and operational dashboards. Troubleshoot production issues, participate in incident response, perform root cause analysis, and implement long-term improvements. Optimize storage, compute, and data movement costs while maintaining platform performance and reliability. Participate in architecture discussions and contribute to technical decisions that improve scalability and system resilience. Create reusable frameworks, tooling, and engineering patterns that improve developer productivity and platform consistency. Conduct thoughtful code reviews and share constructive feedback to maintain high engineering standards. Mentor junior engineers through technical guidance, design discussions, and knowledge sharing. Collaborate with Software Engineering, Product, Analytics, and Machine Learning teams to deliver scalable, high-quality data solutions. Key Qualifications Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field. 6+ years of experience designing and building production-grade data platforms or distributed data systems. Strong SQL skills and deep understanding of data modeling, including dimensional modeling (facts and dimensions). Hands-on experience building production data pipelines using distributed streaming platforms such as Apache Kafka, Amazon Kinesis, or Apache Pulsar, with experience in partitioning, consumer groups, schema management, delivery semantics, and troubleshooting production streaming systems. Strong understanding of batch and streaming data processing concepts, including event time, processing time, windowing, stateful processing, and the trade-offs of exactly-once processing. Proficiency in Python and/or JVM languages such as Java or Scala for building scalable data processing applications. Experience designing and operating modern data lake or lakehouse architectures using Amazon S3 or equivalent object storage, Parquet, and open table formats such as Apache Iceberg, Delta Lake, or Apache Hudi. Experience building cloud-native data infrastructure on AWS, Google Cloud Platform (GCP), or Microsoft Azure, including object storage, streaming and messaging services, IAM, networking, and cost optimization. Experience working with distributed processing frameworks such as Apache Flink or similar streaming technologies. Familiarity with analytical query engines such as ClickHouse or Trino. Experience implementing Change Data Capture (CDC) solutions using Debezium or similar technologies. Experience with modern data transformation frameworks such as dbt is a plus. Familiarity with Infrastructure as Code using Terraform, containerization with Docker, and orchestration platforms such as Kubernetes is a plus. Experience owning production systems, participating in on-call rotations, and resolving production data incidents. Strong communication skills with the ability to document technical decisions, collaborate across teams, and mentor engineers. Passion for building reliable, scalable, and maintainable data platforms. Nice to Have Experience building large-scale streaming architectures using Apache Flink. Hands-on experience with Apache Iceberg, ClickHouse, or Trino in production environments. Experience implementing CDC pipelines using Debezium or similar technologies. Experience using dbt for transformation and analytics engineering workflows. Experience with AWS data services such as MSK, S3, Glue, or Kinesis. Experience deploying infrastructure using Terraform and Kubernetes. Exposure to Machine Learning feature stores or Customer Data Platforms (CDPs). Experience working in high-growth technology companies or on-demand marketplace platforms. Why Toters? Flexible work environment with hybrid-friendly roles. Opportunity to work on a modern, cloud-native data platform that powers products used by thousands of customers every day. Collaborate with talented engineers while growing your technical expertise and career. Strong culture of mentorship, collaboration, and continuous learning. Opportunity to solve complex engineering challenges involving streaming, distributed systems, and large-scale data processing. Direct impact on business-critical products and engineering decisions. 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. 6+ years of experience designing and building production-grade data platforms or distributed data systems. Strong SQL skills and deep understanding of data modeling, including dimensional modeling (facts and dimensions). Hands-on experience building production data pipelines using distributed streaming platforms such as Apache Kafka, Amazon Kinesis, or Apache Pulsar, with experience in partitioning, consumer groups, schema management, delivery semantics, and troubleshooting production streaming systems. Strong understanding of batch and streaming data processing concepts, including event time, processing time, windowing, stateful processing, and the trade-offs of exactly-once processing. Proficiency in Python and/or JVM languages such as Java or Scala for building scalable data processing applications. Experience designing and operating modern data lake or lakehouse architectures using Amazon S3 or equivalent object storage, Parquet, and open table formats such as Apache Iceberg, Delta Lake, or Apache Hudi. Experience building cloud-native data infrastructure on AWS, Google Cloud Platform (GCP), or Microsoft Azure, including object storage, streaming and messaging services, IAM, networking, and cost optimization. Experience working with distributed processing frameworks such as Apache Flink or similar streaming technologies. Familiarity with analytical query engines such as ClickHouse or Trino. Experience implementing Change Data Capture (CDC) solutions using Debezium or similar technologies. Experience with modern data transformation frameworks such as dbt is a plus. Familiarity with Infrastructure as Code using Terraform, containerization with Docker, and orchestration platforms such as Kubernetes is a plus. Experience owning production systems, participating in on-call rotations, and resolving production data incidents. Strong communication skills with the ability to document technical decisions, collaborate across teams, and mentor engineers. Passion for building reliable, scalable, and maintainable data platforms. Experience building large-scale streaming architectures using Apache Flink. Hands-on experience with Apache Iceberg, ClickHouse, or Trino in production environments. Experience implementing CDC pipelines using Debezium or similar technologies. Experience using dbt for transformation and analytics engineering workflows. Experience with AWS data services such as MSK, S3, Glue, or Kinesis. Experience deploying infrastructure using Terraform and Kubernetes. Exposure to Machine Learning feature stores or Customer Data Platforms (CDPs). Experience working in high-growth technology companies or on-demand marketplace platforms. Company Industry InternetE-commerceDotcom Department / Functional Area IT Software Keywords Senior Data Engineer Get real-time job updates only on our App