Principal Data Engineer

Skills

Description

As a senior/principal engineer, you will be responsible for ideation, architecture, design and development of a new enterprise data platform. You will collaborate with other cloud and security architects to ensure seamless alignment within our overarching technology strategy.
Architect and design core components with a microservices architecture, abstracting platform, and infrastructure intricacies.
Create and maintain essential data platform SDKs and libraries, adhering to industry best practices.
Design and develop connector frameworks and modern connectors to source data from disparate systems both on-prem and cloud.
Design and optimize data storage, processing, and querying performance for large-scale datasets using industry best practices while keeping costs in check.
Architect and design the best security patterns and practices.
Design and develop data quality frameworks and processes to ensure the accuracy and reliability of data.
Collaborate with data scientists, analysts, and cross functional teams to design data models, database schemas and data storage solutions.
Design and develop advanced analytics and machine learning capabilities on the data platform.
Design and develop observability and data governance frameworks and practices.
Stay up to date with the latest data engineering trends, technologies, and best practices.
Drive the deployment and release cycles, ensuring a robust and scalable platform.

Responsibilities

• 10+ (for senior) 15+ (for principal) of proven experience in modern cloud data engineering, broader data landscape experience and exposure and solid software engineering experience.
• Prior experience architecting and building successful enterprise scale data platforms in a green field environment is a must.
• Proficiency in building end-to-end data platforms and data services in GCP is a must.
• Proficiency in tools and technologies: BigQuery, Cloud Functions, Cloud Run, Dataform, Dataflow, Dataproc, SQL, Python, Airflow, PubSub.
• Experience with Microservices architectures - Kubernetes, Docker and Cloud Run
• Experience building Symantec layers.
• Proficiency in architecting and designing and development experience with batch and real time streaming infrastructure and workloads.
• Solid experience with architecting and implementing metadata management including data catalogues, data lineage, data quality and data observability for big data workflows.
• Hands-on experience with GCP ecosystem and data lakehouse architectures.
• Strong understanding of data modeling, data architecture, and data governance principles.
• Excellent experience with DataOps principles and test automation.
• Excellent experience with observability tooling: Grafana, Datadog.