PRINCIPAL DATA ENGINEER ( REMOTE)

ADV TECHMINDS; Hyderabad; Remote; INR 2,500,000 - 3,500,000; Full time

Job summary

A leading tech company in Hyderabad is looking for a Principal Data Engineer who will be responsible for designing and developing enterprise data platforms. The ideal candidate should have extensive experience in building data platforms, particularly in GCP. Key responsibilities include collaboration with cross-functional teams and ensuring data quality. This role is perfect for an expert ready to shape the future of data ecosystems.

Qualifications

• 15-20 years of experience in software engineering.
• Exposure to successful enterprise scale data platforms.
• Hands-on experience with GCP and data lakehouse architectures.

Responsibilities

• Responsible for the architecture and development of new enterprise data platforms.
• Collaborate with teams to design data models and storage solutions.
• Design and develop data quality frameworks.

Skills

Data modeling

Data architecture

Proficiency in GCP

Experience with Microservices architecture

Observability tooling: Grafana, Datadog

Tools

BigQuery

Cloud Functions

Cloud Run

Docker

Job description

Overview

PRINCIPAL DATA ENGINEER (REMOTE)

Experiance :15-20 years,

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
• 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

• 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,
• Experience with Microservices architectures - Kubernetes, Docker and Cloud Run
• 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
• 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