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Data Engineer, AI Backend

Pay
$130,000–165,000/yearLocation-specific pay — pay source
Compensation and Benefits We understand that your skills deserve recognition. That’s why we offer a competitive pay scale ranging from $130K–$165K annually, based on experience and expertise. We believe in compensating our dedicated team members fairly for their hard work and commitment. Please note that actual salaries may vary within, above, or below this range based on factors such as education, training, experience, professional achievement, business needs, and location. Toppan Merrill strives to provide our employees and loved ones with competitive benefits including:
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Work setting
Remote — work setting source
Listed location: Work From Home, USA-MN
Read the full posting
Employment
Full-time — employment source
Employment type in the employer’s posting: Full time
From the employer’s posting

What you’ll work on

Full posting
  • Publish and own the AI backend API specification and keep it stable for dependent teams

  • Build a document-context service that respects each attorney's access permissions

  • Build ingestion, processing, and indexing pipelines for SEC filings from EDGAR

  • Create a citation index linking AI answers to exact source sentences in filings

  • Build multi-model AI request routing with logging, monitoring, and cost visibility

From the employer’s posting
Publish and own the AI backend’s API specification, starting with a working stub
Build a document-context service that gives the AI the relevant context from the document an attorney is working on, while respecting that attorney’s access permissions.
Build ingestion, processing, and indexing pipelines for SEC filings from EDGAR to support peer-company analysis.
Create a citation index that connects AI-generated answers to the exact source sentences in the underlying filings.
Build services that route requests across multiple AI models while providing logging, monitoring, reliability controls, and visibility into usage and cost.

What you’ll bring

All qualifications

Core experience

  • 5+ years of professional data engineering or backend engineering experience in Python.
  • Experience building and operating production data pipelines at scale using Databricks, Apache Spark, or a comparable platform.
  • Experience building and operating production Python services (FastAPI or similar), including APIs other engineering teams depend on: publishing, versioning, and maintaining a stable contract.
  • Experience working with text or document data, search, indexing, or information-retrieval systems.
  • Strong data modeling, pipeline reliability, testing, and observability practices.

Preferred experience

  • Experience with retrieval-augmented generation (RAG) or vector/hybrid search in production.
  • Experience building citation, provenance, or source-traceability capabilities for search or AI-generated answers.
  • Experience with SEC filings, EDGAR, XBRL, or other complex financial and regulatory documents.
  • Experience with Azure data and AI services, including multi-model routing, usage monitoring, and cost controls.
Qualification wording
5+ years of professional data engineering or backend engineering experience in Python.
Experience building and operating production data pipelines at scale using Databricks, Apache Spark, or a comparable platform.
Experience building and operating production Python services (FastAPI or similar), including APIs other engineering teams depend on: publishing, versioning, and maintaining a stable contract.
Experience working with text or document data, search, indexing, or information-retrieval systems.
Strong data modeling, pipeline reliability, testing, and observability practices.
Experience with retrieval-augmented generation (RAG) or vector/hybrid search in production.
Experience building citation, provenance, or source-traceability capabilities for search or AI-generated answers.
Experience with SEC filings, EDGAR, XBRL, or other complex financial and regulatory documents.
Experience with Azure data and AI services, including multi-model routing, usage monitoring, and cost controls.

Benefits in the posting

Full benefits wording
  • We understand that your skills deserve recognition. That’s why we offer a competitive pay scale ranging from $130K–$165K annually, based on experience and expertise. We believe in compensating our dedicated team members fairly for their hard work and commitment. Please note that actual salaries may vary within, above, or below this range based on factors such as education, training, experience, professional achievement, business needs, and location.
  • Enhanced Medical, Dental, Vision, and Life insurance for employees and dependents
  • Employer Contribution to 401(k)
  • Paid time off including vacation, sick, community involvement, bereavement, jury duty, and holidays
  • Paid parental leave
  • Paid community involvement/volunteer days
  • Wellness Resources and Employee Assistance Programming
  • Toppan Merrill is an equal opportunity/affirmative action employer. Qualified individuals, including qualified women, minorities, individuals with disabilities and veterans, are encouraged to apply

From the employer’s posting.

Apply at Toppanmerrill

Posting record

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Actively hiringFirst seen by us today, still listed, and not a repost of an earlier posting.

Status in our records
Live
First seen by us
Nov 3, 2026today
Recorded sightings
1

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Toppanmerrill is hiring

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See 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
  • Azure
  • Databricks
  • Datadog
  • Kubernetes
  • Spark
  • FastAPI
Where the posting names them
- Establish data-quality, observability, and operational standards for the AI backend. - Our stack: Python, FastAPI, Databricks (including Vector Search), Spark, LangChain/LangGraph, Azure OpenAI and Azure AI Foundry, Kubernetes (AKS), and Datadog. How You’ll Work
Required Qualifications - 5+ years of professional data engineering or backend engineering experience in Python. - Experience building and operating production data pipelines at scale using Databricks, Apache Spark, or a comparable platform.
- Experience building and operating production data pipelines at scale using Databricks, Apache Spark, or a comparable platform. - Experience building and operating production Python services (FastAPI or similar), including APIs other engineering teams depend on: publishing, versioning, and maintaining a stable contract. - Experience working with text or document data, search, indexing, or information-retrieval systems.
- Experience with SEC filings, EDGAR, XBRL, or other complex financial and regulatory documents. - Experience with Azure data and AI services, including multi-model routing, usage monitoring, and cost controls. - Experience building evaluation datasets or pipelines for generative AI.
- 5+ years of professional data engineering or backend engineering experience in Python. - Experience building and operating production data pipelines at scale using Databricks, Apache Spark, or a comparable platform. - Experience building and operating production Python services (FastAPI or similar), including APIs other engineering teams depend on: publishing, versioning, and maintaining a stable contract.

Your next step

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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

View original posting ↗

Job Description:

Data Engineer, AI Backend 

Shape the Future of AI in Capital Markets and Legal Disclosure 


At Toppan Merrill, we’re building Quinn, a cloud-based platform that helps law firms, investment banks, and public companies draft, review, and file SEC documents, including IPO registration statements, prospectuses, proxy statements, and annual and quarterly reports. 

We’re applying AI to one of the most consequential document workflows in the world, where precision, trust, and accountability matter most. You’ll join a small, highly visible team, work directly with senior engineering leadership, and help create AI capabilities used in complex legal and financial disclosure workflows. 


In this role, you’ll build the data and service foundation behind Quinn’s in-editor AI: the API the editor team builds against, the pipelines and indexes over SEC filings that power peer-company analysis, and a citation layer that traces every AI answer back to the exact sentence it came from. You’ll own both the contract other teams depend on and the systems behind it, in a domain where an untraceable answer isn’t acceptable. 


What You Will Do 


  • Publish and own the AI backend’s API specification, starting with a working stub, then build and operate the production services behind it, keeping the contract stable and communicating changes clearly to the teams that depend on it. 
  • Build a document-context service that gives the AI the relevant context from the document an attorney is working on, while respecting that attorney’s access permissions. 
  • Build ingestion, processing, and indexing pipelines for SEC filings from EDGAR to support peer-company analysis. 
  • Create a citation index that connects AI-generated answers to the exact source sentences in the underlying filings. 
  • Build services that route requests across multiple AI models while providing logging, monitoring, reliability controls, and visibility into usage and cost. 
  • Develop and maintain the pipelines that produce evaluation data used by data scientists to validate AI quality. 
  • Establish data-quality, observability, and operational standards for the AI backend. 
  • Our stack: Python, FastAPI, Databricks (including Vector Search), Spark, LangChain/LangGraph, Azure OpenAI and Azure AI Foundry, Kubernetes (AKS), and Datadog. 

How You’ll Work 


You’ll own the AI backend’s data services and pipelines from initial design through production operation. You’ll work closely with the data scientists developing and evaluating the AI capabilities, the editor team building the customer-facing AI panel, and the platform engineers who own document access and authorization across Quinn. 


First 90 Days 


  • Own the API specification and stub, and keep the contract stable as the editor team builds against it 
  • Stand up the document-context service in a test environment 
  • Get SEC filing ingestion running for an initial set of companies 

 

Required Qualifications 


  • 5+ years of professional data engineering or backend engineering experience in Python. 
  • Experience building and operating production data pipelines at scale using Databricks, Apache Spark, or a comparable platform. 
  • Experience building and operating production Python services (FastAPI or similar), including APIs other engineering teams depend on: publishing, versioning, and maintaining a stable contract. 
  • Experience working with text or document data, search, indexing, or information-retrieval systems. 
  • Strong data modeling, pipeline reliability, testing, and observability practices. 
  • A track record of delivering against firm milestones with cross-team dependencies. 

Preferred Qualifications 


  • Experience with retrieval-augmented generation (RAG) or vector/hybrid search in production. 
  • Experience building citation, provenance, or source-traceability capabilities for search or AI-generated answers. 
  • Experience with SEC filings, EDGAR, XBRL, or other complex financial and regulatory documents. 
  • Experience with Azure data and AI services, including multi-model routing, usage monitoring, and cost controls. 
  • Experience building evaluation datasets or pipelines for generative AI. 

Learn more at Toppan Merrill Careers About Toppan Merrill. 


Compensation and Benefits 


We understand that your skills deserve recognition. That’s why we offer a competitive pay scale ranging from $130K–$165K annually, based on experience and expertise. We believe in compensating our dedicated team members fairly for their hard work and commitment. Please note that actual salaries may vary within, above, or below this range based on factors such as education, training, experience, professional achievement, business needs, and location. 


Toppan Merrill strives to provide our employees and loved ones with competitive benefits including: 

  • Enhanced Medical, Dental, Vision, and Life insurance for employees and dependents 
  • Employer Contribution to 401(k) 
  • Paid time off including vacation, sick, community involvement, bereavement, jury duty, and holidays 
  • Paid parental leave  
  • Paid community involvement/volunteer days  
  • Wellness Resources and Employee Assistance Programming  

 

Toppan Merrill is an equal opportunity/affirmative action employer. Qualified individuals, including qualified women, minorities, individuals with disabilities and veterans, are encouraged to apply


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View original posting ↗

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Pay
Compensation and Benefits We understand that your skills deserve recognition. That’s why we offer a competitive pay scale ranging from $130K–$165K annually, based on experience and expertise. We believe in compensating our dedicated team members fairly for their hard work and commitment. Please note that actual salaries may vary within, above, or below this range based on factors such as education, training, experience, professional achievement, business needs, and location. Toppan Merrill strives to provide our employees and loved ones with competitive benefits including:
Location & working pattern

Work From Home, USA-MN

Preferred Qualifications - Experience with retrieval-augmented generation (RAG) or vector/hybrid search in production. - Experience building citation, provenance, or source-traceability capabilities for search or AI-generated answers.
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