Senior Data Engineer - Paradox Machines

Infinity

6d•$140,000 - $160,000

About The Position

This is a senior engineering role at a data and AI company where you'll be close to the product, customers, and building systems that are used from day one. You'll design and ship the data infrastructure at the core of what the company sells, including modern data platforms, scalable pipelines, and the reporting and AI layers clients use to run their businesses. The role involves building data systems that can serve applications, agents, and people expecting natural language interaction for data queries. This is a client-facing role where you'll lead customer projects, implement solutions, and act as a trusted partner. You will work directly with the CEO and a growing, senior team, expecting to move fast and own a lot.

Requirements

Clear communication. This role is client facing - you'll present to business stakeholders, explain tradeoffs in plain language, and build trust through delivery.
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.
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.

Nice To Haves

Direct experience with how AI changes data platform design is a plus; genuine interest is required.

Responsibilities

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.
Build data systems that serve the expectation of apps, agents, and people who expect to ask questions in natural language and get correct answers.
Lead customer projects, implement solutions, and act as their trusted partner.
Architect and implement data platforms end-to-end, with deep familiarity with modern data architecture (warehouse vs. data lake, table formats, ELT patterns, semantic layers, orchestration) and experience implementing Snowflake, BigQuery, or Databricks in production.
Design data models for business, analytics, and AI consumption, with strong opinions on data modeling best practices (star schema, Data Vault, OBT, etc.) and the judgment to pick the right approach given the business context.
Implement streaming pipelines in production, familiar with technologies such as Kafka, Flink, Redpanda, or similar, understanding operational realities and when batch is the right answer.
Scope requirements, present architecture decisions, and walk stakeholders through tradeoffs.
Communicate clearly with both engineers and business leaders, and be oriented toward results.
Write the query, sketch the schema, or spin up the prototype if that's what moves things forward.