Principal Data Engineer

Remote

Contracted to Full Time

Engineering

Experienced

Please read before applying: We review every application carefully, and all fields (including the required 60–90 second video) must be fully completed and relevant; applications that are incomplete or use AI-generated responses will not be considered. If you’re unable to meet these requirements, we kindly ask that you refrain from applying.

Full-time • Remote

At Boldin, we believe financial confidence should be accessible to everyone. Money decisions shape our lives, yet too often people are left without the clarity or tools they need to make informed choices. We exist to change that.

Boldin is a comprehensive financial planning platform that helps people understand their financial picture, make smarter decisions with their money and time, and plan for the future with confidence.

With over $20M raised and strong momentum, Boldin is entering a pivotal phase of growth. This is an opportunity to join a mission-driven team and help shape the future.

About the Role

The Principal Data Engineer is a senior technical authority responsible for defining Boldin’s data architecture, setting long-term technical strategy, and tackling our most complex data engineering challenges. This role shapes company-wide data standards, and partners with executive and cross-functional leaders to ensure our data platform scales with the business.

Key Responsibilities:

Define and evolve long-term data architecture and vision
Design resilient and scalable data platform and pipelines
Set standards for data modeling, reliability, observability, and governance
Lead complex, high-risk technical initiatives and migrations
Influence tool selection, and technology adoption across the data stack
Elevate engineering excellence
Partner with leadership to align data strategy and business goals
Enable analytics, ML, and product use cases

KPIS + Targets

Uptime: Consistently meets SLA for business-critical pipelines
Freshness: All Tier 1 datasets delivered within SLA
Delivery predictability: Majority of sprint commitments completed as planned
Cost optimization: Year-over-year efficiency improvement as data scales
Documentation: Full coverage for all production-grade assets

Qualifications:

Technical Skills

Bachelor's degree in Computer Science, Engineering, or related field (or equivalent experience)
10+ years of experience in data engineering or related disciplines
Proficient in SQL, Python, or related languages
Cloud Platforms (AWS, GCP)
Strong experience with data warehouse, data lakes, and distributed systems
Strong experience with modern data stack (e.g.,: Athena, BigQuery, Glue, Spark, Dataproc, Kafka, Flink, dbt, Kestra, Fivetran or equivalent)
Proven ability to build and maintain production-grade ELT/ETL pipelines
Experience with workflow orchestration (e.g., Airflow, Dagster, Prefect, Cloud Composer or equivalent)
Experience implementing data quality and observability frameworks
Performance and cost optimization in cloud warehouses

Product & Business Partnership

Experience supporting product analytics and experimentation
Ability to translate business requirements into scalable data models
Strong ownership and accountability for SLAs

Nice to Have

Experience working with Kubernetes
Experience structuring data for ML or AI use cases
Familiarity with Amplitude or product event pipelines
Experience in a high-growth SaaS or fintech environment
Influencing technical direction without direct managerial authority.

Salary Range

$180,000 - $220,000/annual DOE

We want to be as inclusive as possible in our hiring process. If you don’t have 100% of what we’re looking for, but feel like you’re pretty close to growing into the role — please still apply. If a candidate has only 80% of the skills but a fantastic attitude and perspective, they might be the perfect fit.

Boldin values the unique perspectives and contributions of all individuals and we strive to foster a diverse and inclusive culture. We encourage applications from individuals of all backgrounds, including and not limited to people of any race, ethnicity, or national origin, individuals with differing abilities, members of the LGBTQ+ community, veterans, gender minorities, women and people from underrepresented communities.We are committed to providing reasonable accommodations for applicants with differing abilities throughout the recruitment process. If you require any accommodations, please let us know, and we will work with you to meet your needs. We are committed to fostering an environment where everyone can bring their authentic selves to work. If you are comfortable doing so we invite you to share your preferred pronouns in your application.

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Please record a short video response (60–90 seconds) answering the following question and provide a live, accessible link directly to your recording below (YouTube, Loom, Google Drive, Vimeo, etc.):

What specifically motivated you to apply for this role at this time, and what about this opportunity feels aligned with where you are in your career right now?

This does not need to be highly produced—one take and a casual, professional recording are completely fine. We’re simply looking to understand your motivation and alignment.

This doesn’t have to be a traditional video—you’re welcome to get creative with format (slides with voiceover, animation, etc.). We’re primarily evaluating your thinking and communication, not production quality or on-camera presence.

Please note: submissions must be provided as a direct, working link to the recording itself. Links to documents that contain a link, or attachments in place of a live recording link, will be considered incomplete and may not be reviewed.*

Walk us through the most complex data architecture decision you've owned — what was the business context, what tradeoffs did you evaluate (cost, latency, scalability, maintainability), and what would you do differently today?*

Do you have 10 or more years of hands-on data engineering experience, including designing and owning production-grade ELT/ETL pipelines on AWS or GCP?*

Describe a time you turned an ambiguous business problem into a scalable data model or platform decision. What was the problem, how did you align stakeholders, and how did you measure success? (300 words or less)*

Have you personally defined or led the design of a company-wide or org-wide data architecture — not just contributed to one?*

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