Principal Data Engineer

Experience Required: 8+ Years

Roles & Responsibilities:-

· Lead the end-to-end architecture and development of a modern enterprise-scale data platform from the ground up.

· Collaborate with cloud and security architects to ensure platform scalability, performance, and compliance.

· Architect, design, and implement batch and real-time streaming data infrastructures and workloads.

· Build and maintain data lakehouse architectures within the GCP ecosystem.

· Design and develop connector frameworks and reusable data ingestion pipelines to source data from both on-premises and cloud systems.

· Architect and implement metadata management, including data catalogs, lineage, quality, and observability frameworks.

· Design and develop data quality frameworks and governance processes to ensure reliability and accuracy.

· Develop microservices-based components using Kubernetes, Docker, and Cloud Run to abstract platform and infrastructure complexities.

· Design and optimize data storage, transformation, and querying performance for large-scale datasets while ensuring cost efficiency.

· Implement observability tooling (Grafana, Datadog) and DataOps best practices, including CI/CD and test automation.

· Collaborate with data scientists and analysts to define data models, schemas, and advanced analytics capabilities.

· Drive deployment, release management, and platform scalability initiatives.

· Stay ahead of emerging data engineering trends, tools, and best practices to continuously evolve the platform.

Skills:

8+ years of proven experience in modern cloud data engineering and enterprise data platform architecture.

Demonstrated success in architecting and delivering large-scale greenfield data platform

projects.

Deep expertise in Google Cloud Platform (GCP) and its ecosystem — BigQuery, Cloud

Functions, Cloud Run, Dataform, Dataflow, Dataproc, SQL, Python, and Airflow.

Strong understanding of streaming technologies such as Kafka or Pub/Sub.

Hands-on experience with microservices architectures using Kubernetes, Docker, and Cloud Run.

Proven ability to design semantic layers and metadata-driven architectures.

Expertise in data modeling, data architecture, and data governance principles.

Experience with observability and monitoring tools (Grafana, Datadog).

Strong understanding of DataOps principles, including automation, CI/CD, and testing for data

pipelines.

Experience architecting secure, scalable, and high-performance data solutions.

Job Type: Contractual / Temporary
Contract length: 3 months

Pay: ₹130,000.00 - ₹160,000.00 per month

Experience:

• work: 8 years (Required)

Work Location: Remote