Pay
Higher than most similar jobs
$200K–$275K against $127K–$190K, the middle half of what similar US postings state.
From 2,232 postings in the last year. Stated ranges, not salaries paid.
Senior Data Engineer
New York City · San Francisco · New York City, New York, United States
Live, first seen by us 81 days ago- Pay
$200,000–275,000/year · BasePlus equity — pay source
Compensation & Benefits Base salary range: $200,000 to $275,000 annually, depending on experience, plus meaningful equity Parental Leave: 12 weeks fully paid parental leave for all parents, regardless of gender or path to parenthood — no distinction between birthing and non-birthing parent
Read the full posting- Work setting
Hybrid — work setting source
Location type: Hybrid
From the employer’s posting- Employment
Full-time — employment source
Employment type: Full-time
From the employer’s posting- Team
Engineering — team source
Department: Engineering
From the employer’s posting
Before you apply
- Sponsorship
Visa sponsorship not confirmed — sponsorship source
Will you now or in the future require sponsorship for employment visa status (e.g., H-1B visa status)?
From the employer’s application form
What you’ll work on
Full postingBuild monitored CDC pipelines from production MariaDB, PostgreSQL, and MongoDB into S3, Iceberg, and Snowflake
Build a dbt transformation layer so core business metrics come from tested, version-controlled models
Select and implement orchestration with alerting for pipelines and dashboard refreshes
Design and enforce PHI access controls, anonymization pipelines, and account deletion workflows
Support AI team's data needs for model training
From the employer’s posting
Build reliable, monitored CDC pipelines from our production databases (MariaDB, PostgreSQL, MongoDB) into our S3 + Iceberg lake and Snowflake
Stand up a transformation layer (e.g. dbt) on Snowflake so core business metrics (visits, bookings, revenue, retention) come from tested, version-controlled models
Select and implement an orchestration tool so pipelines and dashboard refreshes run automatically, with alerting when they break
Design and enforce the access control model for patient data: row/column-level PHI restrictions, HIPAA Safe Harbor compliance, anonymization pipelines, and account deletion workflows
Support the AI team's data needs for model training
Benefits in the posting
Full benefits wording- Base salary range: $200,000 to $275,000 annually, depending on experience, plus meaningful equity
- Parental Leave: 12 weeks fully paid parental leave for all parents, regardless of gender or path to parenthood — no distinction between birthing and non-birthing parent
From the employer’s posting.
Posting record
Report an error- Status in our records
- Live
- First seen by us
- Aug 20, 202681 days ago
- Recorded sightings
- 63
- Last seen by us
- Nov 9, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Time since we first saw it
Day 81
Up longer than most. Similar jobs usually come down between day 5 and day 54.
From 12,127 similar postings that came down. Coming down is not always a hire.
Doctronic is hiring
1 open job
Open jobs by the week we first saw them, last 12 weeks.
Hiring recordSee how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Python
- SQL
- Airbyte
- AWS
- ClickHouse
- Databricks
- dbt
- Fivetran
- Iceberg
- Metabase
- MongoDB
- PostgreSQL
- S3
- SageMaker
- Snowflake
- Airflow
- Dagster
Where the posting names them
- 5+ years of data engineering experience, including ownership of production data platforms end to end - Strong SQL and Python, with experience building and operating ELT/CDC pipelines (Fivetran, Airbyte, or similar) - Hands-on experience with a modern lakehouse/warehouse stack: S3, Apache Iceberg, a catalog layer, and Snowflake or an equivalent warehouse
- Experience with transformation frameworks (dbt or similar) and orchestration tools (Airflow, Dagster, Glue workflows, or similar) - Solid AWS fundamentals: IAM, Lambda, Kinesis, Glue - A pragmatic, reliability-first mindset
- Experience with HIPAA/PHI data governance, anonymization, or healthcare data - Experience with event/behavioral data pipelines (ClickHouse, GTM/server-side tracking, CDPs) - Familiarity with ML data workflows: feature pipelines, training datasets, notebook environments (SageMaker, Databricks, Jupyter)
- Experience with event/behavioral data pipelines (ClickHouse, GTM/server-side tracking, CDPs) - Familiarity with ML data workflows: feature pipelines, training datasets, notebook environments (SageMaker, Databricks, Jupyter) - Experience with BI tooling (Metabase or similar) and semantic/metrics layers
- Build reliable, monitored CDC pipelines from our production databases (MariaDB, PostgreSQL, MongoDB) into our S3 + Iceberg lake and Snowflake - Stand up a transformation layer (e.g. dbt) on Snowflake so core business metrics (visits, bookings, revenue, retention) come from tested, version-controlled models - Select and implement an orchestration tool so pipelines and dashboard refreshes run automatically, with alerting when they break
- Hands-on experience with a modern lakehouse/warehouse stack: S3, Apache Iceberg, a catalog layer, and Snowflake or an equivalent warehouse - Experience with transformation frameworks (dbt or similar) and orchestration tools (Airflow, Dagster, Glue workflows, or similar) - Solid AWS fundamentals: IAM, Lambda, Kinesis, Glue
What You'll Do - Build reliable, monitored CDC pipelines from our production databases (MariaDB, PostgreSQL, MongoDB) into our S3 + Iceberg lake and Snowflake - Stand up a transformation layer (e.g. dbt) on Snowflake so core business metrics (visits, bookings, revenue, retention) come from tested, version-controlled models
- Strong SQL and Python, with experience building and operating ELT/CDC pipelines (Fivetran, Airbyte, or similar) - Hands-on experience with a modern lakehouse/warehouse stack: S3, Apache Iceberg, a catalog layer, and Snowflake or an equivalent warehouse - Experience with transformation frameworks (dbt or similar) and orchestration tools (Airflow, Dagster, Glue workflows, or similar)
- Familiarity with ML data workflows: feature pipelines, training datasets, notebook environments (SageMaker, Databricks, Jupyter) - Experience with BI tooling (Metabase or similar) and semantic/metrics layers - Prior experience as the first or only data engineer at a startup
Your next step
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- No account needed. “Autofill from resume” at the top of the form fills the key fields from your file.
Already applied? Track this application
Demand for Data Engineer roles
New Data Engineer postings at the employers we follow are up 7% over the last 6 weeks compared with the 6 before.
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
About the role
We file this job under Data Engineer, by the work the posting describes.
Data engineers build and run the pipelines that copy data out of apps and outside services into a warehouse or lake, and fix them when a source changes or a load fails.
Expect SQL, Python, a cloud platform, a scheduler such as Airflow, and often tools for large data such as Spark or Kafka.
An analytics engineer works one step later, turning the raw data a data engineer lands into clean tables people can query.
US government figures for database architects
- Median yearly pay, May 2025
- $139,500
- Expected change, 2025 to 2035
- 9.4% more jobsagainst 3.5% for all jobs
The government has no code for data engineers, so these figures are for the closest group, database architects, which includes data integration and data warehousing specialists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1243.
Job description
The Role
You will be Doctronic's first dedicated data engineer, and you will own the plumbing end to end: how data moves from our production systems into our lakehouse and warehouse, how it gets transformed into trusted, documented tables, and who can access what.
This role serves every team in the company: AI engineering, product, finance, partnerships, and data to name a few.
What You'll Do
Build reliable, monitored CDC pipelines from our production databases (MariaDB, PostgreSQL, MongoDB) into our S3 + Iceberg lake and Snowflake
Stand up a transformation layer (e.g. dbt) on Snowflake so core business metrics (visits, bookings, revenue, retention) come from tested, version-controlled models
Select and implement an orchestration tool so pipelines and dashboard refreshes run automatically, with alerting when they break
Design and enforce the access control model for patient data: row/column-level PHI restrictions, HIPAA Safe Harbor compliance, anonymization pipelines, and account deletion workflows
Establish a single governed copy of production data that analytics, finance, and the AI team all read from
Support the AI team's data needs for model training
Design and build a best-practice warehouse architecture with clean raw, transformed, and business-ready layers powering our executive dashboards
What We're Looking For
5+ years of data engineering experience, including ownership of production data platforms end to end
Strong SQL and Python, with experience building and operating ELT/CDC pipelines (Fivetran, Airbyte, or similar)
Hands-on experience with a modern lakehouse/warehouse stack: S3, Apache Iceberg, a catalog layer, and Snowflake or an equivalent warehouse
Experience with transformation frameworks (dbt or similar) and orchestration tools (Airflow, Dagster, Glue workflows, or similar)
Solid AWS fundamentals: IAM, Lambda, Kinesis, Glue
A pragmatic, reliability-first mindset
Comfort operating with high autonomy and minimal specs in a flat, engineering-first organization
Strong communication skills; you'll work directly with product, marketing, finance, and AI stakeholders
Nice to Have
Experience with HIPAA/PHI data governance, anonymization, or healthcare data
Experience with event/behavioral data pipelines (ClickHouse, GTM/server-side tracking, CDPs)
Familiarity with ML data workflows: feature pipelines, training datasets, notebook environments (SageMaker, Databricks, Jupyter)
Experience with BI tooling (Metabase or similar) and semantic/metrics layers
Prior experience as the first or only data engineer at a startup
Compensation & Benefits
Base salary range: $200,000 to $275,000 annually, depending on experience, plus meaningful equity
Parental Leave: 12 weeks fully paid parental leave for all parents, regardless of gender or path to parenthood — no distinction between birthing and non-birthing parent
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
Compensation & Benefits Base salary range: $200,000 to $275,000 annually, depending on experience, plus meaningful equity Parental Leave: 12 weeks fully paid parental leave for all parents, regardless of gender or path to parenthood — no distinction between birthing and non-birthing parent
- Location & working pattern
New York City, New York, United States
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.