Data Engineer II

Yum!; India; Remote; INR 2,800,000 - 4,800,000; Full time

Job summary

KFC in India (Remote) seeks an experienced Data Engineer to lead complex data initiatives from requirements through production. You will design scalable pipelines and lakehouse architectures using Databricks, Spark, PySpark, SQL, and Python, delivering high-quality data products for analytics.

Join a collaborative team that emphasizes data quality, governance, and scalable patterns, with opportunities to mentor engineers and work across global teams.

Qualifications

• 7+ years of professional experience in data engineering, data warehousing, BI engineering, or related disciplines.
• 3+ years of hands-on experience developing production solutions using Databricks and Apache Spark.
• Advanced proficiency in SQL and strong experience with Python/PySpark.
• Strong hands-on experience with Delta Lake and modern lakehouse engineering patterns.
• Demonstrated experience designing and implementing complex ETL/ELT pipelines.
• Strong understanding of dimensional data modeling, including fact tables, dimensions, surrogate keys, slowly changing dimensions, and analytical data structures.
• Experience designing solutions for large-volume enterprise datasets.
• Demonstrated ability to diagnose and optimize complex SQL, Spark, and data-processing workloads.
• Experience implementing data validation, reconciliation, monitoring, and production support processes.
• Experience working with source-to-target mappings and translating business requirements into technical data solutions.
• Strong analytical, troubleshooting, documentation, and communication skills.
• Ability to independently own technical solutions while collaborating within distributed global teams.

Responsibilities

• Design, build, and maintain scalable data pipelines and processing solutions using Databricks, Apache Spark, PySpark, Python, and SQL.
• Develop robust ingestion, transformation, and publishing processes supporting batch and, where appropriate, near-real-time data workloads.
• Engineer reusable frameworks and patterns that improve development consistency, reliability, maintainability, and speed of delivery.
• Design solutions capable of processing large and complex datasets while maintaining appropriate performance and scalability.
• Troubleshoot complex data pipeline, transformation, performance, and integration issues.
• Optimize Databricks workloads for processing efficiency, reliability, and cost.
• Develop enterprise data solutions leveraging Databricks Lakehouse architecture and Delta Lake.
• Design and maintain structured data layers moving data from raw ingestion through validated, curated, and business-consumption layers.
• Implement scalable transformation patterns using Spark SQL, PySpark, SQL, and Delta Lake.
• Apply Databricks engineering capabilities such as Unity Catalog, workflows/jobs, Delta tables, and data governance controls where appropriate.
• Support performance tuning, workload optimization, monitoring, and production support for Databricks-based solutions.
• Partner with platform and architecture teams to establish repeatable Databricks engineering standards and development patterns.
• Lead technical design discussions and mentor data engineers.
• Collaborate effectively across onshore/offshore engineering, analytics, architecture, product, and business teams.

Skills

Databricks

Apache Spark

PySpark

SQL

Python

Delta Lake

Data modeling

ETL/ELT pipelines

Education

BE / B.Tech in CS or related

Tools

Power BI

Azure

Unity Catalog

Azure Data Lake

Job description

The ideal candidate combines deep hands-on engineering expertise with strong data modeling and solution design skills and can independently lead complex engineering efforts from requirements through production implementation.

Responsibilities

Advanced Data Engineering

• Design, build, and maintain scalable data pipelines and data processing solutions using Databricks, Apache Spark, PySpark, Python, and SQL.
• Develop robust ingestion, transformation, and publishing processes supporting batch and, where appropriate, near-real-time data workloads.
• Engineer reusable frameworks and patterns that improve development consistency, reliability, maintainability, and speed of delivery.
• Design solutions capable of processing large and complex datasets while maintaining appropriate performance and scalability.
• Troubleshoot complex data pipeline, transformation, performance, and integration issues.
• Optimize Databricks workloads for processing efficiency, reliability, and cost.

Databricks & Lakehouse Engineering

• Develop enterprise data solutions leveraging Databricks Lakehouse architecture and Delta Lake.
• Design and maintain structured data layers that move data from raw ingestion through validated, curated, and business-consumption layers.
• Implement scalable transformation patterns using Spark SQL, PySpark, SQL, and Delta Lake.
• Apply Databricks engineering capabilities such as Unity Catalog, workflows/jobs, Delta tables, and data governance controls where appropriate.
• Support performance tuning, workload optimization, monitoring, and production support for Databricks-based solutions.
• Partner with platform and architecture teams to establish repeatable Databricks engineering standards and development patterns.

Advanced Data Modeling

• Design scalable logical and physical data models supporting enterprise reporting, analytics, and downstream data products.
• Develop dimensional models, fact and dimension structures, curated analytical datasets, and reusable semantic-ready data structures.
• Translate complex operational source data into intuitive business-oriented models.
• Establish appropriate grain, keys, relationships, historical handling, and business rules within analytical data models.
• Balance normalization, dimensional modeling, and denormalized analytical structures based on workload and consumption needs.
• Partner with BI and analytics teams to optimize models for Power BI and other analytical consumers.

Data Quality & Governance

• Build automated data validation and reconciliation capabilities into engineering pipelines.
• Establish controls for completeness, accuracy, timeliness, uniqueness, and referential integrity.
• Implement appropriate monitoring, exception handling, logging, and alerting.
• Partner with data governance teams to improve metadata, lineage, documentation, and ownership of enterprise data products.
• Ensure engineering solutions comply with applicable security, privacy, and enterprise data-management standards.

Technical Leadership

• Serve as a senior technical resource for complex data engineering and modeling initiatives.
• Lead technical design discussions and recommend appropriate engineering approaches.
• Perform code and design reviews and help establish development standards and engineering best practices.
• Mentor data engineers and provide guidance on Databricks, Spark, SQL, Python, data modeling, and performance optimization.
• Identify opportunities to simplify legacy processes and modernize existing data solutions.
• Collaborate effectively across onshore/offshore engineering, analytics, architecture, product, and business teams.

Required Qualifications

• 7+ years of professional experience in data engineering, data warehousing, business intelligence engineering, or related disciplines.
• 3+ years of hands-on experience developing production solutions using Databricks and Apache Spark.
• Advanced proficiency in SQL and strong experience with Python/PySpark.
• Strong hands-on experience with Delta Lake and modern lakehouse engineering patterns.
• Demonstrated experience designing and implementing complex ETL/ELT pipelines.
• Strong understanding of dimensional data modeling, including fact tables, dimensions, surrogate keys, slowly changing dimensions, and analytical data structures.
• Experience designing solutions for large-volume enterprise datasets.
• Demonstrated ability to diagnose and optimize complex SQL, Spark, and data-processing workloads.
• Experience implementing data validation, reconciliation, monitoring, and production support processes.
• Experience working with source-to-target mappings and translating business requirements into technical data solutions.
• Strong analytical, troubleshooting, documentation, and communication skills.
• Ability to independently own technical solutions while collaborating within distributed global teams.

Preferred Qualifications

• Experience with Databricks Unity Catalog and enterprise data governance.
• Experience with Databricks workflow orchestration and production deployment patterns.
• Experience with cloud data platforms and object storage, particularly Microsoft Azure environments.
• Experience supporting Power BI and enterprise semantic/reporting solutions.
• Familiarity with CI/CD and automated deployment practices for data engineering.
• Experience integrating REST APIs, relational databases, SaaS platforms, file-based sources, and other enterprise data sources.
• Experience modernizing legacy ETL or traditional data warehouse workloads into Databricks/lakehouse architecture.
• Knowledge of data lineage, metadata management, access controls, and data governance practices.
• Databricks certifications are a plus.

Leadership & Behavioral Competencies

The successful candidate will demonstrate:

• Technical ownership: Takes responsibility for solutions from design through production support.
• Engineering judgment: Selects pragmatic solutions based on scalability, reliability, maintainability, performance, and cost.
• Problem solving: Can independently investigate complex data and platform issues and drive them to resolution.
• Quality mindset: Treats data quality, reconciliation, observability, and documentation as core engineering requirements.
• Collaboration: Works effectively across business, analytics, architecture, BI, and engineering teams.
• Technical leadership: Raises the capabilities of the broader engineering team through mentoring, standards, reusable patterns, and design guidance.
• Business orientation: Understands that successful data engineering is measured by the usability and reliability of the resulting data products, not simply by pipeline completion.

What Success Looks Like

• Deliver reliable, scalable, production-ready Databricks data solutions.
• Reduce complexity by creating reusable engineering patterns rather than one-off pipelines.
• Improve the performance and maintainability of enterprise data workloads.
• Produce well-designed data models that simplify downstream analytics and reporting.
• Improve data trust through automated validation, reconciliation, monitoring, and documentation.
• Provide senior-level technical direction to other engineers while remaining highly hands-on.
• Help advance the Enterprise Intelligence organization toward a more standardized, governed, and scalable lakehouse architecture.

Qualifications

Required Qualifications

• BE with 3-6 years of professional experience in data engineering, data warehousing, business intelligence engineering, or related disciplines.
• 3+ years of hands-on experience developing production solutions using Databricks and Apache Spark.

Job Info

• Job Identification 5785
• Posting Date 11/12/2026, 08:32 AM
• Apply Before 11/30/2026, 12:00 AM
• Degree Level Four Years College
• Job Schedule Full time
• Locations India Gurgaon, Haryana, India (Remote)