Noida Uttar Pradesh Senior validation engineer Jobs – 134 Open Jobs Today

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GCP Senior Data Engineer

HCLTech -
Noida, Uttar Pradesh

Apply Now

Job details

14 days ago

Qualifications

CI/CD
Data modelling
Cloud architecture
Computer Science
Big data
DevOps
Spark
Google Cloud Platform
Master's degree
SQL
Analysis skills
Bachelor's degree
Machine learning
Continuous integration
Scalability
AI
Communication skills
Data warehouse
Python
Hadoop

Full job description

Noida, Uttar Pradesh
Job Summary

We are looking for a Senior Data Engineer with deep expertise in Google Cloud Platform (GCP) to design, build, and optimize scalable data ecosystems. This role is critical in enabling data-driven decision-making by developing high-performance data pipelines, ensuring data reliability, and supporting enterprise-wide analytics initiatives.

You will work closely with cross-functional stakeholders including data scientists, analysts, and business teams to translate business requirements into robust data solutions.

Key Responsibilities

Data Engineering & Architecture

Design, build, and maintain scalable and high-performance data pipelines on GCP.
Develop and manage modern data architectures to support batch and real-time processing needs.
Implement reusable frameworks to standardize data ingestion and transformation processes.

GCP-Based Data Integration

Leverage GCP services such as:
Dataflow
Big Query
Pub/Sub
Cloud Storage
Cloud SQL / Fire store
Build and optimize data integration workflows ensuring efficiency and scalability.

Data Management & Optimization

Optimize storage and query performance across GCP data platforms.
Ensure cost optimization, performance tuning, and scalable data design.

Quality, Governance & Security

Implement robust data quality checks , validation frameworks, and monitoring mechanisms.
Ensure adherence to data governance, security, and compliance standards .

Collaboration & Stakeholder Management

Partner with data scientists, analysts, and business stakeholders to understand requirements and deliver actionable insights.
Translate business problems into technical data solutions.

Operations & Reliability

Monitor, troubleshoot, and ensure high availability and performance of data pipelines.
Implement logging, alerting, and observability frameworks.

Leadership & Mentoring

Provide technical leadership and mentorship to junior engineers.
Drive best practices in coding, architecture, and deployment.

Continuous Improvement

Stay updated with the latest GCP innovations and data engineering best practices .
Proactively recommend improvements to enhance system performance and scalability.

Required Qualifications & Skills

Education & Experience

Bachelor’s or master’s degree in computer science, IT, or related field
5-6 + years of experience in data engineering, with strong exposure to GCP
Skill Requirements

Core Technical Skills (Must Have)

Strong hands-on experience with:
Dataflow, Big Query, Pub/Sub
Cloud Storage, Cloud SQL, Fire store

Expertise in:
ETL/ELT processes
Data warehousing & data modeling

Strong programming skills:
Python, SQL

Key Competencies

Strong analytical and problem-solving skills
Attention to detail and commitment to data accuracy
Ability to work independently and in cross-functional teams
Excellent communication and stakeholder management skills

Preferred (Good-to-Have) Skills

GCP Certifications (Professional Data Engineer / Cloud Architect)
Experience with Big Data technologies (Hadoop, Spark)
Exposure to Machine Learning / AI workflows

Familiarity with DevOps practices & CI/CD pipelines

Other Requirements

Skill Evaluation Matrix

Skill Area

Mandatory

Expected Proficiency (0–5)

Data Pipeline Architecture

Yes

5

GCP Services (Dataflow, Big Query, Pub/Sub etc.)

Yes

5

Data Integration

Yes

4

Data Storage Optimization

Yes

4

Data Quality & Security

Yes

4

Programming (Python, SQL)

Yes

4

Stakeholder Collaboration

Yes

3

Monitoring & Troubleshooting

Yes

3

Leadership & Mentoring

Yes

3

ETL & Data Warehousing

No

3

Big Data (Hadoop/Spark)

No

3

DevOps

No

2

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