Senior Data Engineer
Alchemy is hiring a Senior Data Engineer for a remote role open to applicants in United States. It pays $205k-$230k a year and Level rates it ; you can apply on Alchemy.
Level's read on this job
- Level rates this data engineer role Little AI ●○○○: aI is not part of the work.
- The listed pay starts 37% above the typical data engineer posting on Level.
- The posting lists this role as remote.
- Tools and skills the posting cites include Python, SQL and Snowflake.
- Level lists 21 other open roles at Alchemy.
AI in this role
Build and operate production data pipelines and Snowflake infrastructure to support internal data needs and analytics.
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
How we rate this
Senior Data Engineer at Alchemy rates 10 out of 100 for how much of the daily work is AI. That makes it Little AI (AI Level 1 of 4). The level is about AI in the job, not seniority.
Little AI. AI is not part of the work.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Also common in data engineer jobs at this level
Pay
- This job starts at
- $205k
- Typical data engineer job
- $150k
This posting starts 37% above the typical data engineer job. See data engineer jobs
Questions you could be asked
- Tell me about a project where etl was part of your work. What did you do?
- Tell me about a project where data pipelines was part of your work. What did you do?
- Tell me about a project where data modeling was part of your work. What did you do?
- Tell me about a project where data quality was part of your work. What did you do?
- Walk me through how you've used Python in your day-to-day work.
Adapt your resume
- List these exact terms on your resume: ETL, Data Pipelines, Data Modeling, Data Quality, and Python. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
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