Senior Gen AI Engineer

Weekday; India; Remote; INR 3,000,000 - 5,200,000; Full time

Job summary

Weekday in India is seeking a Data Engineering and Analytics professional to design, develop, and optimize modern data platforms supporting analytics, BI, and AI-driven use cases. You will collaborate with engineering, analytics, product, and business teams to build robust data solutions that scale with evolving business requirements.

The role emphasizes Databricks, data pipelines, cloud technologies, and end-to-end data initiatives from requirements through production support.

Qualifications

• 5+ years of professional experience in data engineering, analytics engineering, data platforms, or a related technology role.
• Experience designing scalable data pipelines and data processing solutions.
• Hands-on with Databricks and modern cloud-based data platforms.
• Strong knowledge of data engineering concepts, data modelling, ETL/ELT, and distributed processing.
• Experience with AWS, Azure, or GCP.
• Strong programming in Python, SQL, Scala, or Java.
• Experience with relational and non-relational databases and large-scale data.
• Understanding of data architecture, integration patterns, performance optimisation, and data quality.
• Experience supporting analytics, BI, ML, or AI use cases.
• Strong analytical and problem-solving and cross-functional collaboration.

Responsibilities

• Design, develop, and maintain scalable data engineering and analytics solutions.
• Build reliable data pipelines for batch and real-time processing.
• Develop data platforms and workflows using Databricks and cloud technologies.
• Work with large and complex datasets to support analytics, reporting, and AI-driven applications.
• Design data models and optimise data processing workflows for performance and scalability.
• Integrate data from multiple structured and unstructured sources.
• Implement data quality, validation, monitoring, and governance practices.
• Collaborate with data scientists, analysts, engineers, product teams, and business stakeholders.
• Translate business requirements into scalable technical and data solutions.
• Troubleshoot data pipeline, processing, performance, and integration issues.
• Optimise existing data architectures and workflows to improve reliability, efficiency, and cost.
• Contribute to cloud-based data architecture and platform modernisation initiatives.
• Develop reusable frameworks, components, and best practices for data engineering.
• Support the deployment, monitoring, and maintenance of data solutions in production environments.
• Stay current with emerging technologies in data engineering, cloud, analytics, AI, and modern data platforms.

Skills

Databricks

Data pipelines

Data processing

Cloud technologies

Python

SQL

Java/Scala

ETL/ELT

Data modelling

Data governance

Analytics

AI/ML data use cases

Big data

Job description

Experience: 5+ yrs

Location: India

Job Type: Full-time

As a Data Engineering and Analytics professional, you will work on designing, developing, and optimising modern data platforms that support analytics, business intelligence, and AI-driven use cases. You will collaborate with engineering, analytics, product, and business teams to build robust data solutions that can scale with evolving business requirements.

The role involves working with cloud platforms, Databricks, data pipelines, data processing, analytics, and modern data architectures. You will contribute to end-to-end data initiatives, from understanding requirements and designing solutions to implementation, optimisation, and production support.

Requirements

Key Responsibilities

• Design, develop, and maintain scalable data engineering and analytics solutions.
• Build reliable and efficient data pipelines for batch and real-time data processing.
• Develop data platforms and workflows using Databricks and modern cloud technologies.
• Work with large and complex datasets to support analytics, reporting, and AI-driven applications.
• Design data models and optimise data processing workflows for performance and scalability.
• Integrate data from multiple structured and unstructured sources.
• Implement data quality, validation, monitoring, and governance practices.
• Collaborate with data scientists, analysts, engineers, product teams, and business stakeholders.
• Translate business requirements into scalable technical and data solutions.
• Troubleshoot data pipeline, processing, performance, and integration issues.
• Optimise existing data architectures and workflows to improve reliability, efficiency, and cost.
• Contribute to cloud-based data architecture and platform modernisation initiatives.
• Develop reusable frameworks, components, and best practices for data engineering.
• Support the deployment, monitoring, and maintenance of data solutions in production environments.
• Stay current with emerging technologies in data engineering, cloud, analytics, AI, and modern data platforms.

What Makes You a Great Fit

• 5+ years of professional experience in data engineering, analytics engineering, data platforms, or a related technology role.
• Strong experience designing and developing scalable data pipelines and data processing solutions.
• Hands-on experience with Databricks and modern cloud-based data platforms.
• Strong understanding of data engineering concepts, data modelling, ETL/ELT, and distributed data processing.
• Experience working with one or more major cloud platforms such as AWS, Azure, or GCP.
• Strong programming and scripting skills in technologies such as Python, SQL, Scala, or Java.
• Experience working with relational and non-relational databases and large-scale datasets.
• Good understanding of data architecture, integration patterns, performance optimisation, and data quality.
• Experience supporting analytics, business intelligence, machine learning, or AI-driven use cases.
• Strong analytical and problem-solving skills with the ability to troubleshoot complex data challenges.
• Ability to work effectively with cross-functional and technical teams.
• Strong communication skills with the ability to explain technical concepts clearly to business stakeholders.
• Experience working in agile, fast-paced technology environments.
• Strong ownership mindset with the ability to independently drive projects from requirements through implementation and production.
• Passion for learning and experimenting with emerging data, cloud, analytics, and AI technologies.