Field Engineer
Posted 2 days ago
The role in short
Embed with clients to architect and build production-grade integrations, automations, and data solutions using Python, Databricks, and AI tools.
Who We Are
Addepar is a global data and AI platform empowering investment professionals to turn complex financial information into actionable intelligence. Addepar unifies portfolio, market and client data in a total portfolio view and delivers AI-powered insights within investment and client workflows. More than 1,500 firms in 60 countries use Addepar to manage and advise on nearly $10 trillion in assets. Its open platform integrates with 650 software, data and consulting partners to power end-to-end investment operations across firms of all sizes and levels of complexity. Addepar supports clients worldwide with offices in New York City, Salt Lake City, São Paulo, London, Edinburgh, Geneva, Warsaw, Dubai, Pune and Singapore.
The Role
As a Field Engineer you will embed directly with clients to architect and build mission-critical integrations, automations, and data solutions. Operating at the intersection of engineering and client success, you will serve as Addepar's technical vanguard - translating complex business requirements into production-grade systems while driving onboarding efficiency and operational scale. You will be the technical expert across Addepar's full solution suite - from API integrations and automation frameworks to advanced data platforms (including our white-labeled Databricks offering, ADX) and AI-driven tooling - solving our clients' most challenging problems. This role combines a forward-deployed model with deep technical expertise in modern software engineering and financial data systems.
Applicants must be legally authorized to work in the United States for any employer without requiring current or future visa sponsorship (for example, employment-based visas such as H-1B, F-1/OPT, or similar), and must be authorized to begin work in the U.S. on their first day of employment.
What You’ll Do
- Deploy directly to client sites (virtual or in-person) to architect, build, and deliver custom integrations, automations, and data pipelines using Addepar's APIs, ADX (Databricks), and modern engineering tools
- Design and implement production-grade solutions that dramatically improve client onboarding efficiency, reduce time-to-value, and enable operational scale
- Build intelligent automation leveraging AI, LLMs, and machine learning to solve complex data transformation, validation, and reconciliation challenges
- Serve as the technical owner for ADX implementations, designing robust data infrastructure within Databricks to streamline client data onboarding and analytics
- Translate ambiguous client requirements into flexible, scalable technical solutions through deep discovery, prototyping, and iterative delivery
- Write production-quality Python code for ETL pipelines, API integrations, data validation frameworks, and automation tooling
- Lead technical workstreams end-to-end, from requirements gathering through production deployment, owning outcomes and client success
- Collaborate with Product, Engineering, and Services teams to identify opportunities for productization and influence platform roadmap
- Develop internal tools, frameworks, and best practices that accelerate delivery across the broader team
- Effectively set, manage, and communicate technical expectations with both client stakeholders and internal cross-functional teams
- Prioritize and context-switch effectively across simultaneous client engagements, seeing each through to successful completion
- Act as a technical advisor and trusted partner to clients, proactively identifying opportunities to drive additional value
Who You Are
- Minimum 3+ years of experience in software engineering, data engineering, or forward-deployed technical roles
- Advanced proficiency in Python with experience building production-grade applications, APIs, and data pipelines
- Strong understanding of REST APIs, authentication patterns, and integration architecture
- Hands-on experience with Databricks, Spark, and distributed data processing frameworks
- Proficiency in SQL and experience working with large-scale datasets across relational and NoSQL databases
- Experience designing and implementing ETL/ELT pipelines for complex data transformations
- Familiarity with AI/ML tools and experience implementing LLM-driven automation solutions
- Strong understanding of financial data domains including portfolio management, performance analytics, and multi-asset class portfolios
- Exceptional problem-solving abilities with a solutions-oriented mindset and bias for action
- Outstanding communication skills with the ability to translate technical concepts for non-technical audiences
- Proven ability to thrive in ambiguous, fast-paced environments while maintaining high-quality standards
- Experience with version control (Git), CI/CD pipelines, and modern software development practices
- Independent, adaptable, and entrepreneurial mindset with a passion for client impact
- Strong work ethic, proactive approach, and high contributing teammate
Addepar takes a market-based approach to pay. A successful candidate's starting pay will be determined based on the role, job-related skills, experience, qualifications, work location, and market conditions. The range displayed on each job posting reflects the minimum and maximum target base salary for this role within the United States.
Compensation for this role includes base salary + bonus + equity + benefits.
Your recruiter can share more about the specific salary range for your preferred location during the hiring process. Additionally, these ranges reflect the base salary only, and do not include bonus, equity, or benefits.
Base Salary Range
$95,000—$149,000 USD
Our Values
- Act Like an Owner - Think and operate with intention, purpose and care. Own outcomes.
- Build Together - Collaborate to unlock the best solutions. Deliver lasting value.
- Champion Our Clients - Exceed client expectations. Our clients’ success is our success.
- Drive Innovation - Be bold and unconstrained in problem solving. Transform the industry.
- Embrace Learning - Engage our community to broaden our perspective. Bring a growth mindset.
In addition to our core values, Addepar is proud to be an equal opportunity employer. We seek to bring together diverse ideas, experiences, skill sets, perspectives, backgrounds and identities to drive innovative solutions. We commit to promoting a welcoming environment where inclusion and belonging are held as a shared responsibility.
We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.
PHISHING SCAM WARNING: Addepar is among several companies recently made aware of a phishing scam involving con artists posing as hiring managers recruiting via email, text and social media. The imposters are creating misleading email accounts, conducting remote “interviews,” and making fake job offers in order to collect personal and financial information from unsuspecting individuals. Please be aware that no job offers will be made from Addepar without a formal interview process. Additionally, Addepar will not ask you to purchase equipment or supplies as part of your onboarding process. If you have any questions, please reach out to ta-operations@addepar.com.
Prep for this interview
5 questions likely to come up at Addepar, each matched to a line in this posting.
- 1Integrations and APIs
They wrote: “Write production-quality Python code for ETL pipelines, API integrations, data validation frameworks, and automation tooling”
Walk me through connecting our product to a customer's internal system that has no documentation.
How to answer
Why they ask: This happens weekly in the job. They want a calm, step-by-step method.
- Find who owns the system and get read access to a test environment
- Watch real traffic or logs to learn the data shapes
- Build a thin adapter with logging around every call
- Agree on how failures get reported, and who fixes what
Tip: Talk about finding the owner of the system first. The code is the easy part.
- 2Discovery and scoping
They wrote: “Translate ambiguous client requirements into flexible, scalable technical solutions through deep discovery, prototyping, and iterative delivery”
A customer says “we want AI in our claims process.” What do you ask in the first meeting?
How to answer
Why they ask: Most FDE work starts this vague. They want to hear you find the real problem before you pick a tool.
- Map the current process: who touches a claim, in what order, with which systems
- Find the slowest or most error-prone step and ask what it costs them
- Agree on one measurable goal (for example time per claim, or error rate)
- Ask what data you can access, and who signs off on security
Tip: Ask about the work, not the technology. Who does it today, how long it takes, what a mistake costs, and how they'd know it got better.
- 3Data and SQL
They wrote: “Experience designing and implementing ETL/ELT pipelines for complex data transformations”
How would you design a daily pipeline that feeds customer data into our product?
How to answer
Why they ask: Tests basic data engineering: scheduling, failures, re-runs and monitoring.
- Extract with timestamps or change tracking, not full dumps, when possible
- Transform in steps you can re-run without creating duplicates
- Validate row counts and key fields before loading
- Alert a person on failure, with enough detail to fix it
Tip: Make re-running safe (same input, same result) and say how you'd know it failed.
- 4LLMs, RAG and agents
They wrote: “Build intelligent automation leveraging AI, LLMs, and machine learning to solve complex data transformation, validation, and reconciliation challenges”
How do you know an LLM feature is good enough to ship?
How to answer
Why they ask: Evals are what separate a demo from production. Many roles list them by name.
- Write 50–200 real examples with expected outcomes, with customer experts
- Agree on a pass bar and the errors that are never acceptable
- Score automatically where possible; review a sample by hand
- Re-run on every prompt or model change, and watch live traffic after launch
Tip: Describe a test set built with the customer, a pass bar agreed up front, and checks that run on every change.
- 5Coding
They wrote: “Advanced proficiency in Python with experience building production-grade applications, APIs, and data pipelines”
Here's a CSV export from a customer. Write a script that cleans it and loads it into our API.
How to answer
Why they ask: Very close to real FDE work. They watch how you handle bad rows and failures.
- Read with a proper CSV parser; check the header first
- Validate each row and collect errors, not crash on the first one
- Send in batches with retries and a clear report at the end
- Write one or two quick tests for the parsing
Tip: Talk through edge cases out loud: encodings, missing fields, duplicates, rate limits.
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