QUALIFICATION

- B.Tech degree/MCA with computer science background or equivalent experience.

EXPERIENCE

- 7 years of total and minimum 3 years of relevant experience in Databricks.

- Proven hands-on experience with Databricks , including designing and implementing scalable data lakehouse solutions.

SKILLS

- Strong expertise in Python and PySpark for developing enterprise-grade data pipelines and distributed processing frameworks.

- Extensive experience with Snowflake, including data modeling, performance tuning, query optimization, and cost management.

- Hands-on experience with Apache Iceberg, Delta Lake, and modern open table formats for large-scale data lake implementations.

- Solid understanding of AWS/GCP cloud services, architecture design, deployment, and platform operations.

- Experience in building batch and real-time data processing pipelines using Spark, Kafka, and cloud-native services.

- Proven expertise in ETL/ELT design, data integration, and cloud migration initiatives.

- Experience leading development teams and delivering data modernization programs.

- Strong troubleshooting and analytical skills with the ability to resolve complex production issues.

- Excellent stakeholder management and communication skills, working across technical and business teams.

- Expert knowledge of Databricks, including Lakehouse Architecture, Delta Lake, Unity Catalog, Workflows, and Performance Optimization.

- Strong proficiency in Python, PySpark, Spark SQL, and distributed data processing.

- Extensive hands-on experience with Snowflake, including Snowpipe, Streams, Tasks, Data Sharing, and Query Optimization.

- Strong expertise in Apache Iceberg, open table formats, schema evolution, partitioning,



and time-travel capabilities.

- Experience designing and implementing large-scale Data Engineering and ETL/ELT Pipelines.

- Hands-on experience with AWS/Azure/GCP cloud-native data services.

- Solid SQL and data modeling skills across OLTP and analytical environments.

- Experience with Real-Time Data Processing using Kafka, Event Hubs, Kinesis, or Pub/Sub.

- Knowledge of Data Governance, Metadata Management, Data Quality, and Security frameworks.

Good to Have

- Experience with Terraform, CloudFormation, or Infrastructure as Code (IaC).

- Knowledge of Docker, Kubernetes, and containerized data platforms.

- Experience with CI/CD tools such as GitHub Actions, Jenkins, Azure DevOps, or GitLab.

- Exposure to AI/ML, MLOps, and GenAI data platform integration.

- Knowledge of Data Mesh, Data Fabric, and modern enterprise data architectures.

- Databricks, Snowflake, and Cloud platform certifications.

- Expert knowledge of Databricks, including Lakehouse Architecture, Delta Lake, Unity Catalog, Workflows, and Performance Optimization.

- Strong proficiency in Python, PySpark, Spark SQL, and distributed data processing.

- Extensive hands-on experience with Snowflake, including Snowpipe, Streams, Tasks, Data Sharing, and Query Optimization.

- Strong expertise in Apache Iceberg, open table formats, schema evolution, partitioning,



and time-travel capabilities.

- Experience designing and implementing large-scale Data Engineering and ETL/ELT Pipelines.

- Hands-on experience with AWS/Azure/GCP cloud-native data services.

- Strong SQL and data modeling skills across OLTP and analytical environments.

- Experience with Real-Time Data Processing using Kafka, Event Hubs, Kinesis, or Pub/Sub.

- Knowledge of Data Governance, Metadata Management, Data Quality, and Security frameworks.

Key Responsibilities

Technical Leadership

- Lead development and implementation of scalable data engineering solutions.

- Drive end-to-end data pipeline development using Databricks and Python.

- Design batch and real-time data processing frameworks.

- Review code and establish engineering best practices.

- Guide teams on performance optimization and reliability improvements.

Data Engineering & Analytics

- Build and maintain ETL/ELT pipelines using Databricks, Spark, and Python.

- Develop scalable data lakehouse architectures.

- Implement data quality, governance, and observability frameworks.

- Optimize Snowflake workloads and data models.

- Design Iceberg-based data lakes for large-scale analytics.

Cloud & Platform Engineering

- Work with AWS/GCP cloud services.

- Develop cloud-native data solutions using managed services.

- Awareness of CI/CD pipelines and Infrastructure as Code.

- Support platform modernization and cloud migration initiatives.

Stakeholder Management

- Partner with architects, product owners, and business stakeholders.

- Translate business requirements into technical solutions.

- Provide effort estimation and delivery planning.

📌 Technical Lead (Bengaluru)
🏢 Carelon Global Solutions
📍 Bengaluru