PRINCIPAL DATA ENGINEER ( REMOTE)

PRINCIPAL DATA ENGINEER

Experiance : 15-20 years, 

Required Skills:Cloud Data Engineering, GCP, Big Query, CloudRun, Dataform, SQL,Python, Airflow,

Pubsub, Kubernetes, Docker, DATAOPS, Grafana, Datadog.

Role Overview

We are seeking a dynamic and highly skilled Principal Data Engineer who has extensive experience

building enterprise scale data platforms and lead these foundational efforts. This role demands

someone who not only possesses a profound understanding of the data engineering landscape but

is also at the forefront of their game. The ideal candidate will contribute significantly to platform

development with diverse skillset while also being very hands-on coding and actively shaping the

future of our data ecosystem.

Responsibilities:

As a principal engineer, you will be responsible for ideation, architecture, design and

development of 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.

Requirements:

15+ 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.

Nice to have:

Experience with Data Mesh architecture.

Experience building Semantic layers for data platforms.

Experience building scalable IoT architectures