Senior Data Engineer

Join Alchemy as a Senior Data Engineer and take ownership of our internal data platform. You will be responsible for ingestion, schema changes, and data quality checks, as well as building batch and streaming pipelines. You will work closely with analytics engineers and data scientists to define datasets for reporting, analysis, and AI-assisted tools. Additionally, you will operate and improve Snowflake, enhance orchestration and deployment workflows, and build data-quality checks. You will also participate in production support and incident response for pipelines and the data platform.

• Construire et gérer la plateforme de données interne qui aide Alchemy à comprendre l'utilisation des produits, les revenus et le coût de service des clients.
• Posséder l'ingestion, les modifications de schéma et les vérifications qui montrent si les ensembles de données partagés sont à jour et corrects.
• Construire des pipelines par lots et en continu qui connectent les bases de données opérationnelles, les événements produits, les systèmes de facturation et les sources externes à la plateforme de données interne.

• Strong SQL and Python skills, including testing, debugging, and maintainable software design
• Understanding of data modeling, incremental processing, schema evolution, and data-quality testing
• Good judgment about stakeholder needs: turn recurring requests into reusable datasets or tooling, and make trade-offs clear
• Experience independently building and operating production data pipelines and the infrastructure around them
• The ability to trace a data discrepancy from a downstream report back to its source, explain the cause, and fix it
• Experience with workflow orchestration and transformation tools such as Airflow and dbt, or equivalent systems
• Experience with Snowflake or a comparable warehouse, including performance tuning and access management
• Kafka, database replication, or change-data-capture systems
• Apache Iceberg, Spark, Trino, or other lakehouse and query technologies
• Usage metering, billing and revenue data, or semantic layers for self-service analytics
• Infrastructure as code, data observability, lineage, and disaster recovery