Job Description
The SpringCube team curated the following job opportunity to help you in your job search. Explore the position below to find your next career move.
Company Overview
A leading live commerce marketplace is building the future of online shopping by connecting buyers and sellers through interactive live commerce experiences. The platform enables sellers to build businesses across a wide range of categories, including trading cards, fashion, electronics, live plants, and more.
Key Responsibilities
- Own data architecture end-to-end, defining how critical business data is captured, modeled, stored, and served.
- Make architectural decisions involving storage formats, compute patterns, scalability, consistency, and service-level agreements.
- Lead the Customer Experience and Logistics data domains.
- Serve as the data owner for mission-critical CX and Logistics concepts, including refunds, customer satisfaction and sentiment, shipping, margin, and related business metrics.
- Design and implement canonical, domain-oriented data models that serve as sources of truth for analytics, machine learning, large language models, and real-time applications.
- Establish and enforce data modeling standards, ownership boundaries, and data contracts across teams.
- Build data quality systems, including testing, lineage, monitoring, reconciliation, and anomaly detection.
- Develop systems that make datasets observable and enable teams to quickly identify and resolve data issues.
- Automate operational workflows and eliminate manual data handoffs between business systems, platforms, data warehouses, and external systems.
- Support the ingestion and operationalization of third-party data sources.
- Enable analytics, machine learning, experimentation, and product engineering teams through high-quality, low-latency data.
- Develop semantic layers, APIs, and real-time query systems that improve access to business data.
- Build platforms that support innovative experimentation and data-driven product development.
- Partner with engineering, product, operations, analytics, and other cross-functional teams to translate complex business requirements into scalable data solutions.
Required Qualifications
- 5+ years of experience as a data engineer or software engineer working with data warehouses, distributed data systems, or event-driven architectures.
- Experience designing and implementing data models using dimensional, Data Vault, or ledger-style approaches.
- Ability to develop data models that support both analytical and transactional workloads.
- Strong hands-on experience with modern data engineering technologies across ingestion, transformation, orchestration, and observability.
- Experience with data ingestion technologies such as Kafka and Debezium.
- Experience with data transformation technologies such as dbt, Spark, or Flink.
- Experience with workflow orchestration tools such as Dagster or Airflow.
- Experience with data observability and quality platforms such as Monte Carlo or Great Expectations.
- Experience operating cloud data warehouses such as Snowflake, BigQuery, or Redshift.
- Knowledge of schema design, cost optimization, and workload tuning for cloud data warehouses.
- Strong production-grade programming skills in Python or SQL.
- Experience integrating data systems with CI/CD pipelines and infrastructure-as-code workflows.
- Strong ability to translate complex and ambiguous business requirements into effective data architectures.
- Ability to collaborate effectively with engineering, product, operations, analytics, and other cross-functional teams.
- Demonstrated ability to work independently, take ownership, and deliver technical solutions in a fast-paced environment.
- Strong focus on measurable outcomes and operational excellence.
Preferred Qualifications
- Experience working with Customer Experience or Logistics data domains.
- Experience operationalizing third-party-created data.
- Experience building real-time data systems and event-driven architectures.
- Experience developing semantic layers and APIs for data access.
- Experience supporting machine learning and LLM-powered applications through high-quality data foundations.
- Experience building experimentation platforms and data products.
- Strong understanding of data governance, data contracts, lineage, and data stewardship.
- Experience balancing data consistency, scalability, performance, and infrastructure costs.
Disclaimer
SpringCube curates tech job listings from various company websites to support tech professionals globally.
- No Endorsement: Job ads on SpringCube do not imply endorsement of their authenticity or quality.
- No Client Relationship: This company is not a client of SpringCube unless stated.
- To Apply: Click the Apply button to be redirected to the hiring company’s application page for this job.
- No Liability: SpringCube is not liable for inaccuracies.
