About Company
Naveera is a trusted global engineering partner delivering Data Engineering, Generative AI, Application Development and IT Infrastructure solutions. With over 15 years of experience in IT services and consulting. we help organizations transform raw data into deployable business value.
Our approach emphasises Production over Pilots we dont stop at proof-of-concepts; we engineer systems that go live, scale reliably, and deliver measurable outcomes. With our CoE-driven delivery model, product engineering DNA, and a scalable talent acquisition engine, we align deeply with enterprise demands for performance, compliance, continuity and innovation.
From Digital Health to Financial Services, and E-Commerce to Technology, we serve a diverse industry base and back every engagement with proven frameworks, low-attrition teams and agile global delivery. At Naveera, we empower clients to turn challenge into growth, data into insight and concepts into platforms.
Specialties
Data Engineering & Modern Data Stack, Generative AI Solutions & Model Deployment, Application Development (Web, Mobile & Enterprise), Artificial Intelligence (Predictive, Conversational, Computer Vision)
Job Title: Senior Data Engineer (GCP Cloud)
Experience: 5 to 15 Years
Job Location: Madurai, Tamil Nadu, India
Shift: 2:00 PM 11 PM IST
Working Mode: Remote/Hybrid
Job Type: Full-Time
Role Overview
We are seeking a skilled and motivated Data Engineer to design, build, and optimize scalable data pipelines and workflows on Google Cloud Platform (GCP). The ideal candidate will play a critical role in enabling efficient data ingestion, transformation, and governance to support analytics and machine learning initiatives.
Key Responsibilities
Data Engineering & Pipeline Development
Design, develop, and optimize data ingestion and transformation pipelines to onboard new clients into the LOS system.
Build and maintain batch and streaming data pipelines using GCP services.
Ensure high performance, scalability, and reliability of data workflows.
Data Quality & Governance
Implement and enforce data quality frameworks, including validation, deduplication, and lineage tracking.
Manage structured and unstructured data workflows with strong governance practices.
Integrate governance tools such as Dataplex for data cataloging and compliance.
Collaboration & Data Enablement
Work closely with ML Engineers and Data Scientists to deliver high-quality datasets for training and evaluation.
Support data-driven decision-making by ensuring timely and accurate data availability.
Automation & Operations
Automate deployment, monitoring, and troubleshooting of data pipelines.
Build robust monitoring solutions to ensure system reliability and quick issue resolution.
Technical Skills & Requirements
Cloud Data Engineering (GCP)
Strong experience with: BigQuery (schema design, partitioning, clustering, query optimization)
Pub/Sub for event-driven data ingestion
Dataflow / Apache Beam for ETL/ELT pipelines
Cloud Composer (Airflow) for workflow orchestration
Cloud Storage for raw and staging data management
Programming & Data Processing
Proficiency in: Python (pandas, pyarrow, fastavro)
SQL (advanced queries and performance tuning)
Experience with: Apache Spark / PySpark (preferred)
Deployment & Automation
Hands-on experience with: CI/CD pipelines (Azure DevOps preferred)
Infrastructure as Code (Terraform)
Docker for containerization
Monitoring & Operations
Experience with: Cloud Logging / Stackdriver for monitoring and alerting
Pipeline performance monitoring and troubleshooting
Preferred Qualifications
Experience working in data-intensive environments or financial services domain
Familiarity with data governance and compliance standards
Exposure to ML data pipelines and feature engineering workflows
Strong problem-solving and analytical skills
Soft Skills
Excellent communication and collaboration skills
Ability to work in a fast-paced, agile environment
Strong ownership mindset and attention to detail
What We Offer
Opportunity to work on cutting-edge cloud data platforms
Collaborative and growth-oriented work environment
Exposure to end-to-end data lifecycle and ML integration
Competitive compensation and benefits