Job description

THE OPPORTUNITY This is a senior engineering role at a data and AI company - which means you'll be close to the product, close to customers, and building systems that are used from day one. You'll design and ship the data infrastructure at the core of what we sell: modern data platforms, scalable pipelines, and the reporting and AI layers our clients run their businesses on. Increasingly, the consumers of our platforms aren't just dashboards and analysts - they're apps, agents, and people who expect to ask questions in natural language and get correct answers. Building data systems that serve that expectation well is a core part of this job. This role is client facing - you'll be leading customer projects, implementing solutions, and act as their trusted partner. You'll work directly with the CEO and alongside a growing, senior team. The problems are real, the feedback loop is short, and the surface area is large. Expect to move fast and own a lot. WHAT YOU'LL BUILD Modern data platforms You'll architect and implement data platforms end-to-end. You have deep familiarity with modern data architecture - warehouse vs. data lake, table formats, ELT patterns, semantic layers, orchestration - and you've implemented Snowflake, BigQuery, or Databricks in production. You know when to use what, and why. Our customers will look to you for recommendations - you should have an equally strong understanding of both first principles data engineering and modern tooling. Data models built for new consumption patterns You're opinionated about data modeling. You've worked with star schema, Data Vault, OBT, etc. and you can argue for the right approach given the business context. You design models for business, analytics and AI consumption. Streaming and real-time systems You've implemented streaming pipelines in production, and are familiar with popular technologies such as Kafka, Flink, Redpanda, or similar. You understand the operational realities of streaming: schema evolution, backpressure, exactly-once semantics, and when batch is the right answer. You understand that with agentic consumption, the need for “near real time” data is more prevalent than ever. Client-facing delivery You'll work directly with client teams - scoping requirements, presenting architecture decisions, and walking stakeholders through tradeoffs. You communicate clearly with both engineers and business leaders, and you're oriented toward results, not just technically elegant solutions. WHAT YOU BRING - Clear communication. This role is client facing - you'll present to business stakeholders, explain tradeoffs in plain language, and build trust through delivery. This is the most important part of the job. - Significant experience building production data systems, with senior-level ownership of architecture decisions. - Deep understanding of modern data architecture and platforms - you've implemented Snowflake, BigQuery, or Databricks, not just used them. - Hands-on knowledge of building scalable and secure data pipelines: orchestration, transformation frameworks (dbt or similar), cloud infrastructure (AWS or GCP). - Strong opinions on data modeling best practices - Kimball, star schema, Data Vault - and the judgment to pick the right one for the problem. - An understanding of how AI changes data platform design: semantic layers, metadata, and governance built so LLMs and agents can consume data reliably. Direct experience here is a plus; genuine interest is required. - Experience implementing streaming pipelines with Kafka, Flink, Redpanda, or similar. - A business orientation and focus on results: you understand that the pipeline exists to answer a business question, and you make tradeoffs accordingly. - A bias for doing: you'll write the query, sketch the schema, or spin up the prototype if that's what moves things forward. WHY THIS ROLE You'll be a senior engineer at a venture-backed company working on one of the most consequential problems in business right now: making company data actually usable - by people, applications and agents. Most engineers spend years building inside someone else's platform. Here, you'll help define what the platform is - and your scope and impact will grow as the company does. WHAT’S IN IT FOR YOU Paradox is committed to fair and competitive pay, ensuring that compensation reflects both market conditions and the value each team member brings. Our salary structure accounts for regional differences in cost of living while maintaining internal equity. For this position, the annual salary ranges by location are: Salary Range $140,000 - $170,000 USD *Offers also include bonus Health and Wellness - Medical, dental, and vision coverage - Unlimited PTO - 401(k) Plan During the interview process, your Talent Acquisition Partner will confirm compensation tier applicable to your location. For candidates outside the U.S., compensation is aligned with local market conditions and cost of living. Total compensation is determined by factors such as location, relevant experience, skills, internal pay equity, and market conditions. While every offer is unique, our compensation philosophy is designed to ensure fairness, consistency, and competitiveness across Paradox Machines. Additional details on total compensation and benefits will be discussed during the hiring process. Equal Opportunity Statement: We’re an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, or veteran status, or any other basis protected by law.

Requirements

Must-haves

This is a senior engineering role at a data and AI company - which means you'll be close to the product, close to customers, and building systems that are used from day one.
Clear communication. This role is client facing - you'll present to business stakeholders, explain tradeoffs in plain language, and build trust through delivery. This is the most important part of the job.
Significant experience building production data systems, with senior-level ownership of architecture decisions.
Deep understanding of modern data architecture and platforms - you've implemented Snowflake, BigQuery, or Databricks, not just used them.
Hands-on knowledge of building scalable and secure data pipelines: orchestration, transformation frameworks (dbt or similar), cloud infrastructure (AWS or GCP).
Strong opinions on data modeling best practices - Kimball, star schema, Data Vault - and the judgment to pick the right one for the problem.
Experience implementing streaming pipelines with Kafka, Flink, Redpanda, or similar.
A business orientation and focus on results: you understand that the pipeline exists to answer a business question, and you make tradeoffs accordingly.
A bias for doing: you'll write the query, sketch the schema, or spin up the prototype if that's what moves things forward.
You'll be a senior engineer at a venture-backed company working on one of the most consequential problems in business right now: making company data actually usable - by people, applications and agents.
Most engineers spend years building inside someone else's platform. Here, you'll help define what the platform is - and your scope and impact will grow as the company does.
For this position, the annual salary ranges by location are:
Health and Wellness

Nice-to-haves

An understanding of how AI changes data platform design: semantic layers, metadata, and governance built so LLMs and agents can consume data reliably. Direct experience here is a plus; genuine interest is required.
Offers also include bonus

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

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Platform: ashby
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Pto

Wellness