Senior Data Engineer — Alchemy

The Bitcoin Street Journal; San Francisco (CA); On-site; USD 140,000 - 190,000; Full time

Job summary

Alchemy is building the data platform to understand product usage, revenue, and the cost of serving customers. You will own ingestion, schema changes, and checks to ensure shared datasets are current and correct, partnering with analytics engineers and data scientists to define datasets for reporting, analysis, and AI tools.

You will build batch and streaming pipelines that connect operational databases, product events, billing systems, and external sources.

Qualifications

• Strong SQL and Python skills with testing and maintainable design.
• Experience with Snowflake or a comparable warehouse and access management.
• Experience with workflow orchestration tools such as Airflow and dbt.

Responsibilities

• Build batch and streaming pipelines connecting operational databases, product events, and billing systems.
• Own ingestion, schema changes, and data quality checks to ensure datasets are current and correct.
• Collaborate with analytics engineers and data scientists to define datasets for reporting and AI-assisted tools.
• Improve orchestration, deployment workflows, and production readiness of pipelines.

Skills

SQL

Python

Data quality

debugging

stakeholder communication

Education

Bachelor’s degree in CS/SE or related field

Tools

Snowflake

Airflow

dbt

Job description

• Company: Alchemy
• Location: San Francisco, CA, USA
• Sector: Blockchain
• Source: web3.career

About Alchemy

Alchemy is the Web3 infrastructure and developer platform that powers millions of users and the world’s most innovative blockchain applications. From enabling $1+ trillion in financial transactions worldwide to scaling L2s and enabling DeFi, Alchemy provides the infrastructure and tools developers need to build reliable, scalable, and accessible experiences.

Trusted by companies like JPMorgan, Stripe, Visa, Franklin Templeton, Nike, and backed by a16z, Coatue, Silver Lake, Lightspeed, and Stanford University, Alchemy is building the foundational layer for a decentralized and AI-enabled Web3 ecosystem. For more information, visit alchemy.com.

The Role

Build and run the internal data platform that helps Alchemy understand product usage, revenue, and the cost of serving customers. The existing Data Engineer intake centers on ingestion, Snowflake operations, workflow orchestration, data quality, and collaboration with analytics partners.

You will own ingestion, schema changes, and checks that show whether shared datasets are current and correct. You will work with analytics engineers and data scientists to define those datasets for reporting, analysis, and AI-assisted tools.

What You Will Do

Build batch and streaming pipelines that connect operational databases, product events, billing systems, and external sources to the internal data platform.

Make ingestion recoverable through change-data capture, schema-change handling, idempotent processing, and safe backfills.

Operate and improve Snowflake, including access controls, workload management, query performance, and compute and storage costs.

Improve orchestration and deployment workflows so engineers can test, ship, and debug pipelines safely.

Build data-quality checks that detect stale, missing, duplicated, or inconsistent records before they reach downstream consumers.

Work with analytics partners on shared data models, source definitions, and documentation. Make dataset ownership and upstream dependencies clear.

Reduce recurring manual work and build reusable tools for self-service data access.

Participate in production support and incident response for pipelines and the data platform. Restore data flow, reconcile affected datasets, and fix the causes of recurring failures.

What We Are Looking For

Experience independently building and operating production data pipelines and the infrastructure around them.

Strong SQL and Python skills, including testing, debugging, and maintainable software design.

Experience with Snowflake or a comparable warehouse, including performance tuning and access management.

Experience with workflow orchestration and transformation tools such as Airflow and dbt, or equivalent systems.

Understanding of data modeling, incremental processing, schema evolution, and data-quality testing.

The ability to trace a data discrepancy from a downstream report back to its source, explain the cause, and fix it.

Good judgment about stakeholder needs: turn recurring requests into reusable datasets or tooling, and make trade-offs clear.

Blockchain experience is not required.

Helpful Experience

Kafka, database replication, or change-data-capture systems.

Apache Iceberg, Spark, Trino, or other lakehouse and query technologies.

Infrastructure as code, data observability, lineage, and disaster recovery.

Usage metering, billing and revenue data, or semantic layers for self-service analytics.

What Success Looks Like

Within three months:

You own a production pipeline and have shipped an improvement to its freshness, reliability, or operating cost.

Downstream users can see whether its data is current, recovery procedures have been tested, and a recurring manual request has become a reusable dataset or tool.

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