This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description As a Data Engineer, you will: - Build and maintain scalable, best-in-class data infrastructure and pipelines that serve as core components of a multi-tenant data platform. - Ensure our data pipelines and data warehouse are optimized for accuracy, performance, and accessibility. - Manage architecture frameworks and participate in the development of data, experimentation, and analytics solutions in collaboration with cross-functional partners in the Product and Engineering organizations. - Implement processes and systems to monitor data quality, ensuring production data is always accurate and available for key stakeholders and business processes that depend on it. - Test and clearly document data assets and warehouse implementations to enable others to understand the implementation and definition of data methodologies easily. - Design data integrations and a data quality framework. - Work closely with Product and Engineering teams to develop a strategy for long-term data platform architecture. Qualifications - Demonstrated ability to build, manage, and optimize core data infrastructure at scale in a multi-tenant environment. - A propensity to independently identify opportunities for optimization and drive forward high-impact projects with minimal guidance. - Proficiency in SQL and strong programming skills in Python, with experience in Bash scripting for automation and workflow management. - Deep knowledge and experience with Snowflake and advanced features like Snowpipes, storage integrations, stages, streams, tasks. - Experience with the AWS ecosystem and securely deploying and managing applications using serverless tools like ECS and Lambda. - Experience building and maintaining custom ingestion pipelines using tools like dlt or requests. - Ingest data from third-party APIs and custom ingestion pipelines with Dagster and Snowflake (Snowpipe, streams, and tasks). - Proficiency with workflow orchestration tools (Dagster or similar tooling like Airflow or Prefect) and data transformation tools (dbt). - Experience with DataOps tools, such as Docker, GitHub Actions, and Terraform. - Experience with AI or ML is a plus. Requirements - Excited to build foundational data infrastructure that powers many e-commerce brands. - Energized by the opportunity to abstract repeated data problems into platform-level solutions. - Passionate about working cross-functionally across engineering, product, and data teams. - Motivated by working in a fast-paced and iterative environment. - Excited by the opportunity to be an early, critical member of a rapidly growing organization. - Personally aligned with our mission to make commerce accessible. Benefits - An investment in your physical and mental well-being; we offer 100% employee Medical Benefits coverage, with 69% dependent coverage. - Flexible PTO; we encourage you to take the time you need to be your best self at work. - An onboarding package and annual work from home stipend to ensure you have everything you need to be successful while working remote. - Generous Parental Leave with customizable transition back to work program. - The benefits of working from home, with opportunities to spend quality time with the team at Chord in-person events throughout the year. - To make an impact! We’re an early-stage company, which means there is space to champion ideas, and create and lead initiatives at any level in the Organization. - This is a full-time, salaried position that includes Equity.

• Implement monitoring systems and routines (data, applications, queries, etc.) • Evolve data models, architecture, and the construction of data pipelines • Implement data migration, processing, and storage routines (ETL) • Develop integrations between different data sources (RDS, external APIs, etc.) • Implement and execute load testing • Monitor running data pipelines

• Own the DataOps lifecycle for our Snowflake-on-AWS platform • Turn data products into reliable services with SLAs/SLOs • Automate promotion across environments • Engineer idempotent pipelines using Streams/Tasks, Snowpipe/Kafka, and orchestration (Airflow/Dagster/Step Functions/Lambda) • Implement the data test pyramid: column/row checks, anomaly detection, reconciliation, and end-to-end validation. • Automate PII classification and object TAGS; enforce tag-based masking, row access policies, RBAC role families, and network policies. • Lead data incident triage, customer comms, RCAs, and post-incident hardening. • Track queries, warehouse utilization, and job cost; implement guardrails.

• Collaborate with stakeholders across the organization to design and implement scalable, cloud-based data solutions, integrating generative AI to drive innovation. • Work closely with cross-functional stakeholders (finance, product, marketing, customer support, tech, data science) to enable trusted data products for internal decision making and external-facing tools. • Take a leading role in the development of a data lake resource to complement our existing data warehouse. • Work with AWS services, automation tools, machine learning, and generative AI to enhance efficiency, stability, security, and performance. • Operate and evolve our Postgres data warehouse: schema design, performance tuning, indexing, access controls, etc. • Build analytics-ready datasets supporting sustainability measurement, supply-chain insights, and business metrics. • Deploy and maintain multiple instances of Cube.dev semantic layers with standardized configuration, CI/CD workflows, and governance practices. • Support integration and deployment of genAI-enabled workflows, especially NLP-based use cases (classification, extraction, normalization, embeddings/similarity). • In collaboration with data scientists, research and develop practical transition plans for evolving selected relational/warehouse data structures into a graph-based knowledgebase.