Software Engineer - Aggregation and Data Enrichment Engineering Hybrid

About us

Powens is the trusted Open Finance partner for financial institutions and fast-growing companies across Europe and Latin America, helping them automate financial workflows, embed payments seamlessly, and turn real-time financial data into actionable decisions.

Headquartered in Barcelona and Paris, Powens operates at the intersection of financial data and payments. Through one platform and two applications, we give our customers the infrastructure to manage finances, automate accounting, assess risk, and grow their businesses.

At Powens, we ask why before we build, we take initiative and see things through, we move from problem to solution together, and we work seamlessly across Europe as one team. If you enjoy pushing things forward rather than accepting the status quo, you'll fit in here.

We are a trusted partner to leading financial institutions and software providers, and we achieve this by hiring exceptional people. Joining Powens means being part of an inclusive, multicultural, and international team of over 130 employees.

• Design, build and run high-traffic REST APIs and backend services in Python, with close attention to latency, throughput and reliability.
• Build and maintain the data pipelines (ingestion, transformation, enrichment) behind our categorization and enrichment services.
• Take ownership of your services from design and implementation through to deployment, monitoring and production support.
• Take part in architectural decisions on a distributed, event-driven, multi-tenant platform.
• Keep the bar high on code quality, security, performance and observability.
• Work with the product team to turn client needs into technical solutions, and with the production team on deployment and operations.

• You can collect data, train models, deploy them, analyze the results and iterate to enhance AI powered features (we do classification with scikit-learn, pytorch)
• Hands-on experience with orchestrated data pipelines. We use Prefect, but equivalent exposure (Airflow, Dagster and others) is just as valuable to us.
• Familiarity with analytical and data lake storage:
  Click House, S3 and Parquet, or equivalent (Big Query, Snowflake ...)

• Experience designing, building and running REST APIs in Python in production, on services under significant traffic.
• Strong software engineering fundamentals: clean architecture, design patterns, testing, code review.
• Experience with asynchronous and distributed processing: queues and workers, retries and backoff, idempotency, at-least-once semantics.
• Good command of relational databases (we use MariaDB): schema design, indexing, query optimization, and migrations

• You can containerize, deploy, size and debug your own services.
• Practical experience with observability: instrumentation with Open Telemetry, and the Grafana suite (Prometheus, Loki, Tempo) or equivalents.
• CI/CD and Git Ops practices
• Experience with cloud platforms is nice to have, AWS in particular (S3, IAM, EC2, EKS).

• Strong problem-solving skills, a pragmatic approach, and the ability to make explicit trade-offs in real-world situations.
• A sense of ownership over what you ship and over how it behaves once it is live.

• Experience with financial data, banking APIs or open banking protocols (PSD2).
• Exposure to machine learning in production. You do not need to have designed the models yourself, but having deployed one, served it behind an API, tracked its accuracy and runtime performance under real traffic, and tuned it as the data and the business needs changed would be nice to have. Our transaction categorization services are a good example of this kind of work.
• Practical experience integrating language or vision models into a product, in particular for structured information extraction from images and PDFs (financial statements, accounting and tax documents).
• Interest in AI-assisted software development, and hands-on use of modern agentic coding tools (Claude Code or similar) across the development lifecycle: exploring an unfamiliar codebase, prototyping, writing and refactoring code, reviewing, testing, documenting and debugging. What matters to us is the judgment that comes with it: knowing where these tools genuinely speed things up, where they need close supervision, and how to stay accountable for what ships.

Women are under-represented in the…