Data Engineering Manager

Felix Technologies

4d•Hybrid

About The Position

At Félix, we are building the indispensable financial companion for Latinos in the US. We combine an AI-powered, conversational-first interface with real-time financial infrastructure to make cross-border money movement as easy as sending a text. Starting with fast, affordable remittances powered by AI and crypto rails, we are expanding into credit, savings, and wallet services to support the complete immigrant financial journey. Our ambition is to deliver a white-glove financial experience with the simplicity of a conversation to a community the traditional financial system has historically overlooked. We are a hyper-growth Series C company, backed by over $300 million in funding from top-tier global investors, including Andreessen Horowitz, QED, Castle Island, Switch Ventures, HTwenty, Monashees, General Catalyst Customer Value Fund. This isn't just about the numbers; it's a testament to the trust our investors have in our vision and our team. Additionally, Félix was selected as an “Endeavour Entrepreneur” and was a recipient of the CrossTech Fintech Startups Award. Joining Félix means you will be part of a team building a legacy, a company that will outlive us all. This is a rare opportunity to apply your skills to a deeply meaningful mission—serving a community that has been underserved for too long because we are obsessed with our customers. We get things done with urgency and focus, driven by extreme ownership over our impact. We collaborate without ego, fostering radical transparency and fierce loyalty so we can grow together. Because we aim for insanely great rather than just good enough, we stay insatiably curious —always experimenting, building the future today, and delivering a product that truly makes our users' lives better. We are looking for a proactive, detail-oriented Data Engineering Manager to lead our core data engineering squad and oversee our DBA team. In this role, you will lead infrastructural platform projects while remaining ready to roll up your sleeves and get your hands dirty alongside your team. We operate in a GCP-native environment and build with an AI-native mindset—using AI to automate code generation, streamline platform operations, and reduce manual friction so our engineers can focus on architecture and business impact.

Requirements

Proven experience leading data engineering teams with a willingness to write code, review PRs, and tackle complex system problems directly.
Strong background in Google Cloud Platform, specifically BigQuery and cloud data processing.
Practical expertise with dbt for transformation models and Apache Flink (or Dataflow) for real-time streaming data.
Active user of modern AI coding assistants and automation workflows; passionate about leveraging AI to scale team impact.
Comfort managing relational database teams (e.g., PostgreSQL/MySQL) without needing to perform deep day-to-day DBA execution.
High standards for clean architecture, platform governance, and documentation.
Excellent communication skills and an advanced English level (Spanish speaker is a nice plus!)
Equivalent competencies in any of the above will also be considered.

Nice To Haves

Spanish speaker

Responsibilities

Lead, scale, and mentor a team of Data Engineers, while providing leadership and high-level management for our DBA team.
Drive critical infrastructure initiatives across streaming and batch environments using GCP, BigQuery, DBT, and Apache Flink.
Champion an AI-first engineering culture—embedding AI into how we write pipeline code, operate platforms, and build data access layers.
Partner with and support our DBA team to ensure transactional database reliability, uptime, and seamless integration with our analytical platform (light technical involvement, heavy strategic alignment).
Set high standards for data quality, testing, observability, and cost optimization across our cloud resources.
Focus on why data and metrics exist—aligning pipeline development directly with key business decisions and product goals.