Principal Data Engineer

Principal Data Engineer

Department

Backend

Employment Type

Full Time

Location

Islamabad, Pak

Description

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.

Key Responsibilities

• 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.

Skills, Knowledge and Expertise

• 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.
• 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.
• 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.