AI Data Engineer

Job Title: AI Data Engineer

Exp- 4 to 8 years

Location- PAN India

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.

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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:

Bachelor's or master's 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.