AI-Augmented Data Engineer

Role : AI-Augmented Data Engineer

Employment Type : Contractual

Job Summary :

We are looking for an AI-Augmented Data Engineer to design, develop and maintain scalable data pipelines and infrastructure while leveraging AI-powered tools to improve engineering productivity and data processing efficiency.

The role focuses on building robust ETL/ELT pipelines, integrating data from multiple sources, optimizing data workflows and implementing automated data processing solutions.

The ideal candidate should have strong expertise in Python, SQL, data engineering frameworks and cloud-based data platforms, along with practical experience using AI-assisted development tools to accelerate coding, debugging, documentation and pipeline optimization.

Key Responsibilities :

- Design, develop and maintain scalable data pipelines for processing structured and unstructured data.

- Build and manage ETL/ELT workflows to extract, transform and load data from multiple sources.

- Leverage AI-powered coding assistants to accelerate pipeline development, SQL generation, debugging and code optimization.

- Validate and optimize AI-generated code to ensure data accuracy, reliability, security and maintainability.

- Develop data transformation workflows using Python, SQL and modern data engineering frameworks.

- Implement batch and real-time data processing solutions based on business requirements.

- Build and maintain data integrations across APIs, databases, cloud storage and enterprise applications.

- Develop and manage data workflows using orchestration tools such as Apache Airflow.

- Work with data warehouses and lakehouse platforms to support analytics and reporting requirements.

- Implement data quality checks, validation rules and monitoring mechanisms.

- Optimize database queries, pipeline performance and resource utilization.

- Collaborate with data scientists, AI engineers, analytics teams and software developers to support data requirements.

- Implement data security, governance, access control and documentation standards.

- Monitor pipeline execution, troubleshoot failures and resolve data inconsistencies.

- Contribute to continuous improvement of data engineering practices through automation and AI-assisted workflows.

Mandatory Skills & Qualifications:

- Strong experience in Data Engineering, Data Pipeline Development or Data Platform Engineering.

- Proficiency in Python and advanced SQL.

- Hands-on experience developing ETL/ELT pipelines and data transformation workflows.

- Practical experience using AI-assisted development tools for coding, debugging, documentation or query optimization.

- Understanding of AI-generated code validation and responsible use of AI coding assistants in production environments.

- Experience with Apache Airflow or similar workflow orchestration tools.

- Strong understanding of data modelling, data warehousing and database concepts.

- Experience working with relational and non-relational databases.

- Knowledge of batch processing and real-time data processing concepts.

- Experience integrating data through REST APIs and external data sources.

- Understanding of data quality, data validation and pipeline monitoring.

- Familiarity with Git, version control and CI/CD practices.

- Knowledge of cloud-based data engineering environments.

- Strong analytical, troubleshooting and problem-solving skills.

Preferred Skills :

- Experience with Snowflake, Databricks, Google BigQuery or Amazon Redshift.

- Familiarity with Apache Spark, PySpark and distributed data processing.

- Knowledge of dbt for data transformation and modelling.

- Exposure to Kafka or similar streaming technologies.

- Experience with AWS, Microsoft Azure or Google Cloud Platform.

- Familiarity with AI coding assistants such as GitHub Copilot, Cursor, Claude Code or equivalent tools.

- Understanding of Generative AI and LLM-based developer productivity solutions.

- Exposure to Docker, Kubernetes and containerized data workloads.

- Knowledge of data governance, metadata management and data observability tools.

- Familiarity with data lake and lakehouse architectures.

Key Skills :

- Data Engineering, AI-Augmented Development, Python, SQL, ETL, ELT, Apache Airflow, Snowflake, dbt, Databricks, Apache Spark, PySpark, Data Pipelines, Data Modelling, Data Warehousing, Data Integration, REST APIs, Kafka, AWS, Azure, BigQuery, Git, CI/CD, GitHub Copilot, Cursor, Claude Code, Data Quality, Data Governance