Python Software Engineer (Data Engineering)

What you'll own Production-grade Python ingestion pipelines for a global data marketplace operating at scale: 120M+ files, 1.4 TB of data delivered weekly, dozens of active data products. You design and build the systems that move data reliably from ugly real-world sources into a governed, high-availability estate. Your code runs in production, not in a backlog. How we work Critical Propulsion consultants bring a different standard to wherever they work. Our model is built around swarm delivery and AI-amplified output with talent dense teams, and that's the lens you carry into every engagement. Your first engagement embeds you directly within a client team, where you'll adapt to their environment while holding your own bar for how fast and how well work gets done. What we expect You write Python the way it was meant to be written. Class hierarchies, abstract base classes, dependency injection, polymorphism. You architect master controller / services / helpers structures and you can explain exactly why you made each design choice. You build RESTful API integrations that don't break. Authentication flows, pagination, error handling, retry logic. You've been burned by flaky third-party APIs before and your code reflects it. You write SQL for real work. Querying, transformation, pipeline validation. Not just SELECT *. You build ingestion pipelines that hold up in production. Fault-tolerant, resilient, modular. You think about observability and traceability before you write the first line. You work on Azure data infrastructure without hand-holding. ADLS, Azure SQL, and the patterns that connect them. You operate autonomously. No status meetings that could be a message. No decks that could be a decision. You communicate directly. When something is broken or the spec is wrong, you say so with evidence. You embrace agentic development. You don't treat AI as autocomplete. You delegate real work to AI agents, review their output critically, and iterate fast. What we don't filter on Years of experience as a number. If you can ship production Python pipelines in 5-day pulse cycles, the number on your resume is irrelevant. Specific tooling as a prerequisite. This first engagement is Python-heavy by design, but we hire to a team, not a role. Broad engineering judgment and the instinct to pick up whatever the work demands is what sets consultants apart here. Deep expertise in one thing is a starting point, not a finish line. Pedigree. No school or company name substitutes for demonstrated ability to build pipelines that don't fall over in production. Nice to have Azure Databricks and PySpark for high-volume distributed processing scenarios. Familiarity with XBRL and iXBRL financial data formats. Git/GitHub fluency and experience working in trunk-based or short-lived branch workflows. Exposure to near real-time processing patterns. What you get A team where everyone builds. No layers of management between you and the work. AI agents as real teammates. You'll use AI tooling to accelerate development, testing, and documentation. Pulse cycles that create natural rhythm without traditional sprint overhead. Direct client impact. Your pipelines move real data to real consumers in days, not quarters. Competitive comp sized for senior consultants, not blended-team billing rates. How to apply Show us something you built. A pipeline design, a hard ingestion problem you solved, a code pattern you're proud of. Skip the cover letter unless you actually want to write one. Real conversation. No fluff.