Senior Data Platform Engineer

About Tala

At Tala, we’re applying advanced technology and human creativity to solve what legacy institutions can’t or won't. We are a global financial infrastructure company on a mission to unleash the economic power of the global majority, recognizing that today’s financial infrastructure doesn’t work for most of the world’s population.

We’re the first and only platform to combine the intelligence of a credit bureau, the payments execution of a fintech, and the relationship expertise of a bank into one vertically integrated solution. Our platform is powered by an expansive moat of proprietary data and AI/ML decisioning technology, enabling us to deliver instant, reliable liquidity personalized to every customer's needs. Through our flagship credit app, we’ve disbursed over $7 billion in credit to more than 12 million customers across Latin America, Southeast Asia, and East Africa. These customers have leveraged Tala products to start and expand small businesses, manage day-to-day needs, and pursue their financial goals.

Our pioneering work and proven impact have earned us consistent recognition, including being named to:

CNBC’s Disruptor 50 for five years. CNBC’s World's Top Fintech Companies for two consecutive years. Forbes’ Fintech 50 list for nine consecutive years.

Visionary investors, persuaded by the economic power of the global majority, have committed half a billion dollars in equity and debt to Tala's success.

Given the global nature of our team, we operate on a remote-first approach with office hubs in Santa Monica, CA (HQ); Nairobi, Kenya; Mexico City, Mexico; Manila, the Philippines; and Bangalore, India.

Most Talazens join us because they connect with our mission. If you are energized by the impact you can make at Tala, we’d love to hear from you!

The Role

The Data Platform Engineering team is building the foundation for a best-in-class decision engine—empowering data scientists, machine learning engineers, and analytics teams to deliver insights and intelligence at scale.

As a Data Platform Engineer, you’ll design, build, and evolve the infrastructure and tooling that enable our internal teams to move fast, work efficiently, and make data-driven decisions. You’ll collaborate closely with cross-functional partners to ensure reliability, scalability, and ease of use across the data ecosystem. You’ll also help guide backend and frontend teams in adopting an Event-Driven Data Mesh architecture, ensuring consistency and high-quality data flows throughout the organization.

What You'll Do Design, build, and maintain scalable data infrastructure to power analytics, machine learning, and business intelligence use cases. Develop and evolve data pipelines and streaming systems using technologies like Kafka, Spark, AWS Kinesis, and Flink. Implement infrastructure as code using Terraform or CloudFormation across AWS, GCP, or Azure environments. Partner with Data Scientists, MLEs, and Analytics Engineers to ensure data accessibility, quality, and reliability. Champion best practices for DataOps, observability, and reliability within the data platform. Contribute to architectural decisions and help drive adoption of an Event-Driven Data Mesh across teams. What You'll Need 5+ years of experience in software engineering, with strong proficiency in Python. 3+ years of experience building and maintaining streaming data systems (Kafka, Spark, AWS Kinesis, Flink). Proven experience deploying infrastructure as code using Terraform or CloudFormation. Background in a software or platform engineering environment, ideally supporting internal developer or data teams. Advanced degree in Engineering or a related technical field. Prior experience working remotely in a distributed team environment. Preferred Experience (Nice to Have) Experience building and maintaining data lakes, ideally using Delta Lake or Apache Iceberg. Experience collaborating with Machine Learning Engineers or Data Scientists on Feature Store design and maintenance. Familiarity with SRE principles and DevOps/DataOps best practices.