Developer in Data Engineering

Job Description 

At Workleap, we’re transforming how teams work by turning scattered knowledge into smart, actionable insights. Our AI-powered products surface the right information at the right time, across platforms and tools our customers use every day.

We’re looking for developers in data engineering who thrive on solving complex integration challenges and building secure, scalable pipelines to support a variety of analytics and AI use cases. Your work will enable governed, high-impact usage of data across our ecosystem.

You’ll collaborate with data engineers, data scientists, and app developers to ensure data is accessible, reliable, and protected, thus contributing to the foundations of a product that blends intelligence with control.

So, what will your new role look like?

Workleap is seeking a developer in data engineering with strong Python development skills and a solid understanding of data workflows, APIs, and modern cloud-native software practices.

Responsibilities:

• Develop and maintain Python services, libraries, and utilities to support data workflows and platform-level orchestration.
• Use Airbyte and Dagster to build and manage ingestion and transformation pipelines.
• Implement reliable integrations with external APIs and cloud services.
• Apply object-oriented design principles and leverage modern Python tools and conventions (e.g., FastAPI, Pydantic) to write maintainable, production-grade code.
• Collaborate on data modeling, schema design, and contract management between services.
• Ensure security, including access controls, secrets management, and pipeline isolation.
• Contribute to internal platform components that support AI integration, including retrieval-augmented generation (RAG) pipelines and data preparation for embeddings.
• Participate in pipeline observability and testing, including logging, versioning, and validation strategies.
• Work cross-functionally to deliver robust, accessible data that powers our SaaS analytics and AI features.

What does your future team look like?   

This is a hybrid role that blends data engineering and application development, ideal for those who want to help build and evolve the foundations of a modern data platform while working hands-on with real-world engineering tools.

You will play a key role in maintaining and improving our Python-based orchestration stack, supporting both traditional ELT pipelines and AI-driven applications, including RAG-style AI agents, and agentic solutions.

This is a great opportunity to work alongside senior developers, grow your architectural instincts, and contribute to an infrastructure that supports analytics, automation, and intelligent SaaS features.

What are the next challenges awaiting your team?   

• Delivering new features continuously for a high-impact product in a fast-paced, dynamic environment.
• Enforcing security rigorously, both in data modeling and technical implementation: security is not an afterthought here, but a core design principle!
• Staying current with an evolving tech landscape.

Qualifications

• Strong experience with Python and object-oriented programming.
• Proficient in HTTP API consumption and integration, including handling pagination, authentication, and rate limits.
• Comfortable working with modern Python tooling and conventions, including FastAPI and Pydantic.
• Hands-on experience with Airbyte and Dagster, used as part of structured pipeline development.
• Good understanding of data modeling, schema evolution, and API/data contract management.
• Solid awareness of security practices in a data engineering context (e.g., RBAC, credential management, scoped execution).
• Comfortable with Git and collaborative software engineering practices.

Nice to have

• Exposure to data warehouse technologies (e.g., Snowflake, BigQuery, Redshift).
• Experience with infrastructure-as-code (e.g., Terraform, Pulumi).
• Familiarity with containerized environments (Docker, Kubernetes).
• Understanding of RAG pipelines, vector search, or AI feature engineering workflows.
• Interest in dbt, Snowpark, or related data transformation tooling.