Senior Backend Engineer | Python & Data-Intensive Systems

Description Kendago is on a mission to redefine how consumers go from noticing a need to buying the right solution online. In an internet shaped by AI, fragmented attention, and constantly shifting platforms, we build, own, and scale top-performing brands and offers - both our own and those of leading partners — by combining proprietary technology, data-driven decision making, and creative excellence at performance scale. We operate at consumer scale, reaching millions of people every day across paid channels, owned funnels, and digital touchpoints. In a world where creative trends, consumer behavior, and platform algorithms shift overnight, Kendago stays ahead of the curve. We move fast, think deeply, and build with purpose. Our culture is rooted in entrepreneurship, innovation, and collaboration - along with shared wins, team dinners, and a whole lot of good food and laughter. We’re a team of doers, and boundary-breakers - product, data, R&D, growth, and creative talent who own the full stack of how consumer brands are built and scaled today. After 17 years in the game, every funnel, every test, every brand decision happens in-house - which means your work moves the needle directly and visibly. If you’re ready to tackle meaningful challenges, grow at lightning speed, and help shape the next frontier of consumer brand building, you belong here. About the role We are looking for a Senior backend engineer who is strong in Python and enjoys building reliable, data-intensive systems. You will help develop our next-generation platform, integrations with external data sources, and the services that collect, process, and expose data throughout our product. This is primarily a backend engineering role—not a pure data engineering or DevOps position. However, you should be comfortable working with data pipelines and taking services through the full lifecycle, from design and implementation to production monitoring. This is a fully Remote position. Requirements 5+ years of professional backend development experience , with strong Python proficiency. Hands-on experience building production APIs and services with FastAPI, Django, Flask, or a comparable framework. Strong SQL skills, including schema design, query optimization, and working with large or complex datasets. Experience designing reliable backend systems that process data asynchronously or in batches. Solid understanding of distributed-system fundamentals, including failure handling, retries, idempotency, concurrency, and eventual consistency. Experience building and consuming REST APIs and integrating with third-party systems. Practical experience operating services in AWS or a comparable cloud environment. Strong problem-solving skills and the ability to investigate unfamiliar systems and complex data flows. Familiarity with Docker, CI/CD, logging, monitoring, and troubleshooting production systems. Ability to work independently, make sound engineering decisions, and collaborate well with a small team. Nice to Have- Experience with Amazon Redshift or another analytical data warehouse. Experience building ETL or ELT pipelines and implementing data-quality checks. Experience with queues, schedulers, or workflow orchestration tools. Familiarity with infrastructure as code and cloud infrastructure management. Experience with high-volume data ingestion, analytics platforms, marketing technology, or campaign-management systems. Responsibilities Design, build, and maintain backend services using Python and FastAPI . Develop reliable integrations with external APIs, platforms, and data providers. Build and improve data-collection and processing workflows, including scheduled, asynchronous, and long-running workloads. Contribute to our new platform and capabilities such as algorithm execution and data collection orchestration. Design efficient data models and queries for operational databases and analytical systems such as Amazon Redshift. Handle real-world integration challenges including rate limits, retries, partial failures, idempotency, and changing external schemas. Improve the performance, scalability, observability, and reliability of existing services. Own features end to end: technical design, implementation, testing, deployment, monitoring, and ongoing improvement. Collaborate with product and engineering colleagues to turn ambiguous requirements into pragmatic technical solutions.