We are looking for a hands-on Data Engineer with 3+ years of experience building production-grade data pipelines, cloud data platforms, and automated data workflows. You are comfortable working across structured, semi-structured, and unstructured data; you understand the importance of data quality, lineage, security, and cost optimization; and you are excited to build the data foundation required for modern AI, ML, and GenAI use cases.

Minimum Qualifications:

- Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, Engineering, Statistics, Mathematics, or related technical field.
- 3+ years of hands-on experience in data engineering, ETL/ELT development, data warehousing, or cloud-based data platform delivery.
- Strong proficiency in SQL and Python for data extraction, transformation, automation, testing, and production support.
- Experience designing and operating scalable pipelines on cloud platforms such as Google Cloud Platform, AWS, Azure, or equivalent enterprise data ecosystems.
- Experience with modern data platforms and tools such as Big Query, Spark, Dataflow, Dataproc, Airflow/Cloud Composer, Dataform, DBT, or similar technologies.
- Valuable understanding of data modeling, dimensional modeling, partitioning, clustering, performance tuning, and cost optimization.
- Working knowledge of data quality frameworks, monitoring, alerting, metadata, lineage,



and production support practices.
- Familiarity with Git, CI/CD, agile delivery, code reviews, documentation, and reusable engineering standards.
- Strong communication skills with the ability to explain technical solutions clearly to engineering, analytics, and business stakeholders.

Preferred Qualifications:

- 5+ years of experience delivering enterprise data engineering solutions in cloud-native environments.
- Experience building data products for AI/ML, GenAI, semantic search, retrieval-augmented generation, feature engineering, or model monitoring use cases.
- Experience working with unstructured data such as documents, logs, text, images, transcripts, or embeddings, and preparing them for downstream AI consumption.
- Hands-on experience with Data Ops, MLOps enablement, pipeline observability, automated testing, and production incident resolution.
- Experience migrating legacy workflows from Hadoop, Alteryx, or on-premise platforms to modern cloud services.
- Experience with APIs, microservices, event-driven architectures, streaming data, or real-time analytics.
- Cloud certifications in Google Cloud Platform, AWS, Azure, or relevant data engineering technologies.
- Experience mentoring junior engineers, defining engineering standards, or contributing reusable platform accelerators.

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📌 AI GCP Data Engineer (India)
🏢 Ford Motor
📍 India