AI Data Engineer

Job Title: AI Data Engineer Exp- 4 to 8 years Location- PAN India Job Description Key Responsibilities: Build and maintain data infrastructure: Design and construct scalable, reliable data pipelines, storage, and processing systems in the cloud. Ensure data quality: Clean, transform, and enrich raw data to create \"business truth\" that AI models can use for accurate insights. Enable AI/ML: Make data readily available and optimized for consumption by AI and machine learning models. Manage cloud services: Work with cloud-specific services for storage, compute, and networking to build an efficient and scalable AI data environment. Implement security and governance: Apply security controls to protect data and ensure compliance within the data platforms. Monitor and optimize: Continuously monitor data workloads and optimize for performance and cost-effectiveness. Essential skills and tools Cloud Platforms: Deep knowledge of data services at least one major cloud provider (e.g., AWS, Google Cloud). Programming Languages: Strong proficiency in Python, Spark and SQL. Data Warehousing & Storage: Experience with technologies like Azure Synapse Snowflake, GCP BigQuery, Databricks, AWS Redshift and Data Lake. Data Pipelines: Familiarity with tools like Azure Data factory, AWS Glue, Apache Airflow, Kafka and dbt for orchestrating data workflows. AI-specific tools: Knowledge of vector databases Infrastructure as Code (IaC): Skills in tools like Bicep, Terraform or CloudFormation to automate infrastructure deployment. CI/CD: Understanding of continuous integration and continuous deployment pipelines. Experience with any of the following Cloud Native Data Services: Azure: Azure Data Factory, MS Fabric, Azure Databricks, Azure Synapse Analytics, Datalake Gen2 and Azure Dedicated SQL Pool (ADW), Cosmos DB AWS: AWS Glue, AWS S3, AWS Athena, AWS Kinesis and AWS Redshift, Dynamo DB Google Cloud Platform (GCP): GCP Dataproc, GCP DataFlow, GCP BigQuery, GCP Cloud Storage, Cloud SQL and Pub Sub, Google BigTable, Google Spanner. Qualifications: Bachelors or masters degree in engineering or technology Proven experience in building and deploying ETL/ELT solutions in production. Strong understanding of Data models and Data pipelines and cloud-native Big data architectures. Excellent problem-solving, communication, and collaboration skills.