> Markdown version of [/jobs/ext/2208855-data-engineer-remote](https://www.wearedevelopers.com/jobs/ext/2208855-data-engineer-remote). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer - Remote - **Company:** SUNDAE, INC. - **Location:** United States (Remote available) - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Airflow, BigQuery, Business Systems, Cloud Database, Data Architecture, Data Systems, Data Vault Modeling, Data Warehousing, Distributed Data Store, Python (Programming Language), Machine Learning, SQL Databases, Systems Integration, Snowflake, Apache Spark, Data Layers, Event Driven Architecture, Debezium, Low Latency, Apache Flink, Apache Kafka, Data Pipelines - **Published:** August 24, 2026 - **Apply:** https://arc.dev/remote-jobs/j/redirect/pecg87mzaa ## About the Role * Minimum of 3+ years of experience as a data or software engineer building data warehouses, distributed data systems, or event-driven architectures. * Proficiency in designing and implementing data models using dimensional, Data Vault, or ledger-style techniques supporting analytical and transactional workloads. * Deep hands-on expertise with modern data tooling such as Kafka, Debezium, dbt, Spark, Flink, Dagster, Airflow, Monte Carlo, and Great Expectations. * Experience operating cloud data warehouses like Snowflake, BigQuery, or Redshift, including schema design, workload tuning, and cost optimization. * Proficiency in writing production-grade code in Python or SQL and integrating with CI/CD pipelines and infrastructure-as-code workflows. * Ability to collaborate across disciplines-engineering, product, and analytics-to translate complex business requirements into effective data systems. * Self-starter mentality with the ability to thrive in a fast-paced environment, owning both technical design and operational outcomes. ## Description Data is at the core of Whatnot's mission to foster community and connection through commerce. As our newest Data Engineer, you will be instrumental in building and scaling the data systems that support data-driven decision-making across the organization. You will collaborate directly with stakeholders in payments and finance teams to design reliable, scalable data architectures, develop resilient pipelines, and create foundational data products that drive growth and innovation. On a day-to-day basis, your responsibilities will include owning end-to-end data architecture, defining how we capture, model, and serve critical business data, and implementing these designs in production. You will develop and operate streaming and batch data workflows to process high-volume events related to user activity, transactions, marketing performance, and operational telemetry, ensuring low latency, data completeness, and accuracy. Additionally, you will create domain-oriented data models that serve as the source of truth for analytics, machine learning, and real-time applications, establishing standards and ownership boundaries across teams. Enforcing data quality at scale will be a key focus, involving building tests, lineage, monitoring, and reconciliation systems to ensure datasets are observable and anomalies are actionable. You will also automate operational workflows in partnership with business systems and platform teams to eliminate manual data handoffs and reconcile data across services, warehouses, and external systems. Supporting analytics, machine learning, and product teams by exposing high-quality, low-latency data through semantic layers, APIs, and real-time query systems will be essential to your role. Your work will directly influence product growth, insights, and innovation, making your contributions critical to Whatnot's ongoing success., * Own data architecture end-to-end, including defining data capture, modeling, and serving strategies, and implementing them in production environments. * Develop and operate streaming and batch data pipelines to process high-volume events with guarantees on latency, completeness, and accuracy. * Create and maintain canonical data models that serve as authoritative sources for analytics, machine learning, and real-time applications. * Build and maintain data quality systems, including tests, lineage, monitoring, and reconciliation tools to ensure dataset reliability and observability. * Automate operational workflows by partnering with business systems and platform teams to streamline data processes and eliminate manual handoffs. * Support analytics, ML, and product teams by providing high-quality, low-latency data through APIs, semantic layers, and real-time query systems. * Continuously evaluate and improve data architecture, pipelines, and models to support business growth and product innovation. ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Python-Based Data Streaming Pipelines Within Minutes](https://www.wearedevelopers.com/videos/1233-python-based-data-streaming-pipelines-within-minutes) - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) - [Making Data Warehouses fast. 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