Senior Data Engineer Data & AI New York · Remote +1 $205K–$230K/yr 13 Oct 2026

Hood L2; New York (NY); Remote; USD 120,000 - 160,000; 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 0

Job summary

Alchemy is building the data platform that powers product usage and revenue insights. You will own ingestion, schema changes, and checks to ensure datasets are current and correct, collaborating with analytics engineers and data scientists.

As a Data Engineer, you'll build batch and streaming pipelines connecting operational databases, product events, billing, and external sources, while optimizing Snowflake, orchestration with Airflow/dbt, and data quality checks.

Qualifications

• Experience independently building and operating production data pipelines and the infrastructure around them.
• Strong SQL and Python skills, including testing and debugging.
• Experience with Snowflake or a comparable data warehouse and with performance tuning and access management.
• Experience with orchestration and transformation tools such as Airflow and dbt.
• Understanding of data modeling, incremental processing, schema evolution, and data-quality testing.
• Ability to trace data discrepancies from downstream reports to source and fix root causes.
• Ability to translate stakeholder requests into reusable datasets or tooling.

Responsibilities

• Build batch and streaming pipelines that connect operational databases, product events, billing systems, and external sources to the internal data platform.
• Ensure ingestion recoverability via change-data capture, schema changes, idempotent processing, and safe backfills.
• Operate and improve Snowflake, including access controls, workload management, and cost optimization.
• Improve orchestration and deployment workflows so engineers can test, ship, and debug pipelines safely.
• Build data-quality checks to detect stale, missing, duplicated, or inconsistent records before downstream use.
• Collaborate with analytics partners on shared data models, source definitions, and documentation; clarify ownership and dependencies.
• Reduce recurring manual work by building reusable self-serve data tools.
• Participate in production support and incident response for pipelines and the data platform.

Skills

SQL

Python

Data modeling

Data quality

Tools

Snowflake

Airflow

dbt

Apache Iceberg

Spark

Trino

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