We’re hiring a hands-on Principal Data Engineer who can both architect and build large-scale data systems.
You’ll design the blueprints for modern data pipelines, then write the code to bring them to life. From event-driven ingestion to analytics-ready datasets, your work will power the data backbone behind Emumba’s AI and product platforms.
This is a technical IC position, not a managerial one. You’ll spend most of your time writing code, optimizing data pipelines, and mentoring engineers through real-world implementation.
- Design and code robust data pipelines for batch and real-time use cases.
- Define data models, schemas, and evolution strategies for scalable systems.
- Implement reliable ingestion, transformation, and storage layers across cloud services.
- Work hands-on with event-driven architectures, microservices, and data APIs.
- Optimize data quality, performance, and observability.
- Partner with AI and backend teams to make data discoverable and usable in production.
Contribute directly to architecture, code reviews, and performance tuning.
Must-Have Skills
- 6+ years of hands-on experience in designing and building production data systems.
- Strong coding in Python and proficiency in SQL.
- Experience with data modeling, ETL/ELT, and data lifecycle management.
- Familiarity with event-driven systems (Kafka, Kinesis, or similar).
- Practical experience with cloud-native data workflows (AWS S3, Lambda, Glue, or similar).
- Solid understanding of structured, semi-structured, and unstructured data formats.
Nice-to-Have
- Experience working with vector stores or retrieval-based pipelines.
- Familiarity with Lakehouse concepts (Delta/Iceberg/Hudi — or similar patterns).
- Understanding of data governance, cataloging, and lineage tracking.
- Exposure to ML data preparation or feature store design.
Soft Skills
- Hands-on builder attitude: owns delivery from architecture to deployment.
- Collaborates across AI, backend, and DevOps teams.
- Provides mentorship through examples, not supervision.
Communicates clearly and pragmatically about technical trade-offs.
