Data Engineer II

ISA; Maharashtra; On-site; INR 1,200,000 - 2,400,000; Full time

Job summary

ISA is seeking a data engineer to design and deliver robust, scalable data pipelines and infrastructure for analytics and AI workloads. You will own end-to-end pipeline domains, contribute to architectural decisions, and drive engineering standards across cloud environments.

You will work with Data Scientists and Analytics Engineers to translate data needs into maintainable solutions, ensure data quality, and mentor junior engineers while documenting pipelines and models.

Qualifications

• Bachelor's degree in Computer Science or related field.
• Fluent in English.
• Proficiency in SQL and Python for data transformation.
• Experience with data orchestration tools (Airflow, dbt).
• Understanding of data modelling techniques (dimensional, star/snowflake).
• Familiarity with batch and streaming processing (Spark, Kafka, Azure Event Hubs).
• Knowledge of Git, CI/CD and automated testing.

Responsibilities

• Design, develop, and maintain scalable ETL/ELT pipelines across cloud environments.
• Own assigned pipeline domains end-to-end from design to deployment and monitoring.
• Implement data models to support analytics, BI, and ML needs.
• Build orchestration workflows using Airflow, dbt, or Azure Data Factory.
• Optimize queries and pipelines on Snowflake and Azure Synapse.
• Define data quality rules, automate testing, and set up alerts.

Skills

SQL

Python

Airflow

dbt

Data modeling

Spark

Kafka

Azure Event Hubs

Git

CI/CD

Communication

Education

Bachelor's degree in CS/CE/IT or related field

Tools

Azure Data Factory

Snowflake/Azure Synapse

AWS/GCP data services

Job description

Job Purpose

Design and deliver robust, scalable data pipelines and infrastructure components that ensure reliable, high-quality data availability for analytics, data science, and AI workloads. The role operates with growing technical ownership, taking end-to-end responsibility for assigned platform domains and contributing meaningfully to architectural decisions and engineering standards.

Design and deliver robust, scalable data pipelines and infrastructure components that ensure reliable, high-quality data availability for analytics, data science, and AI workloads. The role operates with growing technical ownership, taking end-to-end responsibility for assigned platform domains and contributing meaningfully to architectural decisions and engineering standards.

Key Result Responsibilities

• Design, develop, and maintain scalable ETL/ELT pipelines that ingest, transform, and serve data from structured and unstructured sources across cloud environments
• Own assigned pipeline domains end-to-end — from requirements and design through to deployment, monitoring, and iterative improvement
• Implement and evolve data models that support analytics, BI, and machine learning consumption requirements
• Build and maintain orchestration workflows using tools such as Apache Airflow, dbt, or Azure Data Factory
• Optimize pipeline and query performance across cloud data platforms including Snowflake and Azure Synapse
• Define and implement data quality rules, automated testing, and alerting to ensure reliability and consistency of data outputs

Key Result Responsibilities-Continued

• Contribute to architectural discussions and platform decisions, providing well-reasoned technical input and trade-off analysis
• Collaborate with Data Scientists, Analytics Engineers, and business stakeholders to translate data requirements into maintainable engineering solutions
• Conduct code reviews and support the development of Associate and DE I engineers through practical guidance
• Maintain clear documentation for all assigned pipelines, data models, and infrastructure components

Qualifications (Academic, Training, Languages)

• Bachelor's degree in Computer Science, Computer Engineering, Information Technology, or a related field.
• Fluent in English Language.
• ITIL Certification is an advantage but not mandatory.
• Strong proficiency in SQL and Python for data transformation, automation, and pipeline development.
• Working knowledge of data orchestration and transformation tools (e.g., Apache Airflow, dbt).
• Understanding of data modelling techniques — dimensional modelling, star/snowflake schema, and medallion/lakehouse architecture patterns.
• Familiarity with batch and streaming data processing concepts (e.g., Spark, Kafka, Azure Event Hubs).
• Good grasp of software engineering fundamentals: version control (Git), CI/CD practices, and automated testing.
• Ability to communicate technical designs clearly and provide constructive feedback to junior team members.

Work Experience

• With 4–6 years of hands-on experience in data engineering or a closely related software engineering role
• Demonstrable experience delivering production-grade data pipelines in a cloud environment.
• Solid hands-on experience with cloud data platforms on Azure, AWS, or GCP (e.g., Azure Data Factory, Synapse Analytics, S3, Redshift, BigQuery).
• Experience with data warehousing platforms such as Snowflake or Azure Synapse, including query optimisation and access control.