Senior Data Engineer

Alchemy Health; New York (NY); On-site; USD 160,000 - 175,000; Full time

Benefits offered by this job

Private Health Insurance

Gym Reimbursement

Commuter Benefits

Flexible Time Off

In-Office Group Meals

Learning & Development Stipend

Company Sponsored Conferences & Events

Books & Audiobook subscription with 1B

Job summary

Alchemy is seeking a Data Engineer to build and operate the internal data platform that powers product usage, revenue, and cost insights. You will own ingestion, schema changes, and checks, collaborating with analytics engineers and data scientists to define datasets for reporting, analysis, and AI-assisted tools.

You will work with Snowflake, Airflow, and dbt to deliver reliable pipelines, implement data-quality checks, and enable self-service datasets for downstream teams.

Qualifications

• Experience 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; able to trace discrepancies to source and fix root causes.
• Good judgment about stakeholder needs: deliver reusable datasets or tooling and communicate trade-offs clearly.

Responsibilities

• Build batch and streaming pipelines connecting databases, product events, billing, and external sources.
• Make ingestion recoverable with CDC, schema changes, idempotent processing, and safe backfills.
• Operate and optimize Snowflake, including access controls, WM, and cost management.
• Improve orchestration and deployment workflows for safe testing, shipping, and debugging.
• Create data-quality checks to detect stale, missing, duplicated, or inconsistent records.
• Collaborate with analytics partners on data models, source definitions, and documentation.
• Define dataset ownership and upstream dependencies; reduce manual work with reusable tools.
• Participate in production support and incident response for pipelines and the data platform.

Skills

SQL

Python

Data modeling

Incremental processing

Schema evolution

Data quality testing

Stakeholder engagement

KPI reporting

Kafka

Spark

Iceberg

Trino

Tools

Airflow

dbt

Snowflake

Job description

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.

Benefits & Perks

Private Health Insurance

Gym Reimbursement

Commuter Benefits

Flexible Time Off

In-Office Group Meals

Learning & Development Stipend

Company Sponsored Conferences & Events

Books & Audiobook subscription with Bookster

Compensation

$160K – $175K Offers Equity Offers Bonus