Senior Data Engineer (APAC Region)

ANRGI TECH; Maharashtra; On-site; INR 2,500,000 - 3,800,000; Full time

Job summary

ANRGI TECH in Maharashtra, India, seeks an experienced Data Engineer to design, build, and operate scalable data pipelines on the Databricks platform, developing end-to-end workflows from ingestion to consumption with robust monitoring and error handling.

You will refactor legacy code to PySpark, migrate ETL to ELT patterns on Databricks, and collaborate with data architects and analysts to deliver high-quality data products. Proficiency in Python, SQL, Delta Lake and Spark required.

Qualifications

• Minimum 5 years of data engineering experience.
• Hands-on PySpark and Databricks experience.
• Strong Python and SQL skills.
• Experience with Delta Lake and Spark SQL.
• Familiarity with CI/CD and version control.

Responsibilities

• Design, build, and operate scalable data pipelines on Databricks.
• Develop end-to-end data workflows from ingestion to consumption.
• Implement monitoring, alerting, and error handling.
• Refactor legacy code to PySpark and migrate ETL to ELT.
• Collaborate with data architects, analysts, and stakeholders.

Skills

PySpark

Databricks Platform

Delta Lake

Python

SQL

Data Modelling

Data Engineering principles

Structured Streaming

CI/CD

Git

Data Governance

Tools

Databricks

Spark

Git

CI/CD

Job description

• Design, build, and operate scalable and reliable data pipelines on the Databricks platform
• Develop end-to-end data workflows from ingestion through transformation to consumption
• Implement robust error handling, monitoring, and alerting mechanisms
• Ensure data pipeline reliability, performance, and maintainability
• Optimize pipeline performance through efficient Spark job design and cluster configuration
• Manage and orchestrate complex data workflows using Databricks Jobs and workflows

Data Pipeline Development & Operations

• Design, build, and operate scalable and reliable data pipelines on the Databricks platform
• Develop end-to-end data workflows from ingestion through transformation to consumption
• Implement robust error handling, monitoring, and alerting mechanisms
• Ensure data pipeline reliability, performance, and maintainability
• Optimize pipeline performance through efficient Spark job design and cluster configuration
• Manage and orchestrate complex data workflows using Databricks Jobs and workflows

Legacy Code Modernization

• Refactor legacy code and data pipelines to PySpark for improved performance and scalability
• Migrate traditional ETL processes to modern ELT patterns on Databricks
• Assess existing codebases and identify opportunities for optimization and modernization
• Ensure backward compatibility and data integrity during migration processes
• Document refactoring approaches and create migration playbooks
• Collaborate with stakeholders to minimize disruption during code transitions

Data Engineering Excellence

• Implement data quality checks and validation frameworks
• Design and maintain Delta Lake tables with appropriate optimization strategies
• Develop reusable code libraries and frameworks for common data engineering tasks
• Follow software engineering best practices including version control, testing, and CI/CD
• Participate in code reviews and provide constructive feedback to team members
• Troubleshoot and resolve data pipeline issues in production environments

Collaboration & Knowledge Sharing

• Work closely with data architects, analysts, and business stakeholders
• Collaborate with Infrastructure (Infra), Applications (Apps), and Cyber teams
• Share knowledge and best practices with Team *****
• Mentor junior data engineers on PySpark and Databricks technologies
• Document technical solutions and maintain comprehensive documentation

Essential Technical Skills

• Data Engineering: Strong foundation in data engineering principles, ETL/ELT processes, and data pipeline design patterns
• PySpark: Proven hands-on experience developing data pipelines using PySpark, including DataFrames API, Spark SQL, and performance optimization
• Databricks Platform: Practical experience with Databricks workspace, cluster management, notebooks, and job orchestration
• Workspace AI Agent: Knowledge of Databricks Workspace AI Agent capabilities and integration
• Data Modelling: Experience implementing data models including dimensional modelling, data vault, or lakehouse architectures
• Delta Lake: Understanding of Delta Lake features including ACID transactions, schema evolution, and optimisation techniques
• Python: Strong Python programming skills for data processing and automation

Additional Technical Skills

• SQL proficiency for data querying and transformation
• Experience with cloud platforms (Azure, AWS, or GCP)
• Understanding of data governance and security best practices
• Knowledge of streaming data processing (Structured Streaming)
• Familiarity with DevOps practices and CI/CD pipelines
• Experience with version control systems (Git)
• Understanding of data quality frameworks and testing methodologies

Professional Experience

• Minimum 5 years in data engineering or related roles
• At least 2-3 years of hands‑on experience with Databricks platform
• Proven track record of refactoring legacy code to modern frameworks
• Experience building and maintaining production data pipelines at scale
• Background working across multiple data sources and formats
• Experience in agile development environments

Required Certifications - mandatory to have at least one certification

• Databricks Certified Data Engineer Associate OR Databricks Certified Data Engineer Professional

Additional Certifications (Preferred)

• Databricks Certified Associate Developer for Apache Spark
• Cloud platform certifications (Azure Data Engineer Associate, AWS Certified Data Analytics, or Google Cloud Professional Data Engineer)
• Relevant data engineering or big data certifications

Soft Skills

• Strong problem-solving and analytical thinking abilities
• Excellent communication skills to explain technical concepts clearly
• Ability to work collaboratively in cross-functional teams
• Self‑motivated with strong attention to detail
• Adaptable to changing priorities and technologies
• Client‑focused mindset with commitment to quality delivery