Cloud AI Data Engineer
At a glance
Mid-level Data Engineering role at Fa Ewjt Saasfaprod1. Noida · full-time.
Pay not stated Data Engineering salary
dataroles summary, based on the employer's posting.
What you'll do
- Build and maintain AI-focused data pipelines on GCP and Snowflake, keeping them scalable for analytics and AI work
- Create ETL workflows that ingest, transform, check, and connect structured data for analytics and AI use
- Generate synthetic datasets reflecting business scenarios, unusual cases, and real-world data patterns for model development and testing
- Develop APIs and backend workflows so AI applications can access, connect, and coordinate data
- Add monitoring, quality checks, automation, and CI/CD, while tuning pipeline performance and cloud resource costs
What you bring
- You have three to six years of practical data engineering, ETL, and cloud data platform experience
- You have built AI-ready pipelines with GCP and Snowflake, and can produce synthetic data that reflects real patterns and edge cases
- You bring hands-on Python and SQL skills, plus experience with structured data, data modeling, and data quality
- You have developed APIs or backend services and understand workflow orchestration for AI or analytics systems
Who this fits
This mid-level role suits a hands-on engineer who can work across data science, engineering, product, business, and client-facing teams. The position is hybrid in Noida, India.
From the employer
As a Cloud AI Data Engineer, you will design, build, and optimize AI-ready data pipelines across GCP, and Snowflake. You will work closely with data science, engineering, and business teams to integrate structured data, create high-quality synthetic datasets, and support scalable AI solutions through robust ETL pipelines, APIs, and backend workflows. This role requires strong hands-on expertise in cloud-native data engineering, performance tuning, cost optimization, and production-grade data systems that enable advanced analytics and AI use cases.
- Design, build, and maintain scalable AI-ready data pipelines across GCP, and Snowflake.
- Develop and optimize ETL workflows to ingest, transform, validate, and integrate structured data for analytics and AI solutions.
- Create and curate synthetic datasets that reflect real-world data patterns, edge cases, and business scenarios for AI model development and testing.
- Build APIs and backend workflows to enable seamless data access, integration, and orchestration for AI-driven applications.
- Collaborate with data scientists, ML engineers, product teams, and business stakeholders to understand data requirements and deliver reliable data solutions.
- Implement monitoring, quality checks, automation, and CI/CD practices to improve reliability, scalability, and operational efficiency.
- Optimize pipeline performance, storage, compute usage, and cloud costs across modern data platforms.
- 3–6 years of hands-on experience in data engineering, ETL development, and cloud-based data platforms.
- Strong experience building AI-ready data pipelines across GCP, and Snowflake.
- Expertise in designing and generating synthetic datasets that capture real-world patterns, anomalies, and edge cases.
- Strong hands-on experience with ETL pipelines, structured data integration, data modeling, and data quality frameworks.
- Proficiency in Python, SQL, and cloud-native services for building scalable and production-ready data systems.
- Experience developing APIs, backend services, and workflow orchestration components that support AI and analytics solutions.
- Good understanding of performance tuning, pipeline scalability, cost optimization, and cloud resource management.
- Familiarity with CI/CD, version control, monitoring, logging, and automation practices in data engineering environments.
- Strong communication skills with the ability to work effectively with technical teams, business stakeholders, and client-facing groups.
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