Job: Data Engineer

Company Overview

Our client equips CFOs and finance teams with the tools to be proactive with machine intelligence, real-time data, and predictive insights. Finance is a critical part of every enterprise. It sits at the center of almost all decisions and data flows and is charged with identifying the levers to improve business performance, and leading and validating capital allocation decisions. CFOs act as consiglieres to Boards and executive teams and wield a level of influence and autonomy in organizations that is only rivaled by the CEO. Despite this, most finance teams can only access software and services that still reflect capabilities from decades ago. Our client is changing this.

Our vision is to unlock the true power of finance by giving teams the tools to be proactive. We will shift finance from reactive decision-making to predictive and prescriptive reasoning — from the “what” to the “why”, the “so-what”, and the “what-if?” — and bring the leading edge of machine learning to finance.

We’re an experienced team of builders and we’re bringing together engineers, data scientists, product and design taste-makers, financiers, and others from a variety of backgrounds to experiment with new ideas and rethink old assumptions to create a new world for our customers. Come build with us!

About the Role

As an early member of the engineering team at our client, you’ll help define and establish our foundational data engineering and data science practices, drive the technology roadmap, and be a leader on a team that will grow quickly. Your hands-on contributions will have a huge impact on the product and our customers. You’ll also have the opportunity to contribute meaningfully to many other areas of the business and you will shape our culture, values, and ways of working in foundational ways.

What will You be Doing in the Role?

You will bring data engineering to the forefront of product development and decision making, building and improving on data pipelines for our financial intelligence engine, product KPIs, and forecasting applications. You will develop scalable solutions for data ingestion, processing, and management. You will also develop infrastructure automation solutions to build, run, and monitor various environments including sandbox, staging, and production. You’ll obsess about engineering quality and execution decisions to increase customer value at a high-velocity and to seamlessly onboard new customers. You will be on the ground floor of our client’s engineering team and you will mentor and lead junior members as the team scales. You’ll also work closely with engineering and product leaders to set long-term strategy, and build and recruit engineering and machine learning teams.

What would make You a Good Fit?

You’re both relentless and kind, and don’t see these as being mutually exclusive You have a self-directed learning style, an insatiable curiosity, and a hands-on execution mindset You have deep experience working with product and engineering teams to launch products powered by complex data and machine learning You have developed scalable automated data pipelines using orchestration systems such as Airflow You have deep experience with relational databases and SQL, specifically PostgreSQL, as well as data lakes You continuously raise the bar on development practices such as code quality tools, build tools, etc. You obsess about correctness and performance You have excellent writing and speaking skills with a specific knack for connecting technical choices to product opportunities

Must-Have Qualifications

• 4+ years of experience in Python
• 3+ years of experience working with Pandas
• 2+ years developing ETL pipelines to support aggressive product demands while simultaneously onboarding new customers
• 2+ years of experience with SQL and Postgres to manage and connect datastores
• Experience developing end-to-end processes for Data Science to go from idea to production
• Experience setting up a data lake from scratch
• Proficient with Docker and experience working in a Kubernetes environment
• You’ve partnered with DevOps or platform engineers to deliver complete and high-performant data platform infrastructure for all environments
• You naturally think quantitatively about problems and work backward from a customer outcome

What’ll make you stand out (but not required)

• You have experience with open-source projects and the AWS stack
• You have experience building ML models
• You have a strong connection to finance teams or closely related domains, the challenges they face, and deep appreciation for their aspirations