Staff Data Engineer
Posted Updated 8
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Summary
A hands-on principal-level individual contributor who designs, builds, and optimizes large-scale batch and real-time data pipelines, defines data models and schemas, and mentors engineers — powering Emumba's AI and product platforms. Core stack: Python, SQL, Kafka/Kinesis, and AWS cloud data services (S3, Lambda, Glue).
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.
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.