Zorba is the new age analytics provider that brings data driven decision to life for its clients. We believe in getting analytics executed on ground with the client team rather than just creating complex machine learning or predictive modelling algorithms. 

We offer services across the spectrum ranging from analytics training and setting up data centres to delivering analytics based strategic consulting solutions. One of the key value adds we do is with the speed at which we deliver solutions.

Headquartered in Mumbai, Zorba was conceived with an objective of creating value for businesses in near future rather than long term. We are experts in leveraging big data, building data driven solutions and operationalizing analytics at each stage of decision making process with following key offerings: 

•	Data Visualization and Dashboard design
•	Predictive Modelling Algorithms
•	Data Management and Transformation
•	Analytics based reporting solutions
•	Marketing and Business Process Optimization

Witnessing the stories of success and failure of numerous businesses at different stages, we came to the conclusion that every business can be successful if improvisation continues relentlessly. 

We want to make this process faster and smarter for our clients in their quest for growth by hand holding through data analytics and strategic leadership for effective decision making. 

You can follow us on :
https://www.quora.com/profile/Yash-Chaudhary-516
https://www.instagram.com/zorbaconsulting/?hl=en
https://twitter.com/ConsultingZorba
https://www.facebook.com/Zorbaconsulting/

Zorba Consulting India

5–10 years Data Engineering
<ul>
<li>
Strong Scala programming
</li>
<li>
Strong Apache Spark
</li>
<li>
Strong Apache Kafka
</li>
<li>
Spark Structured Streaming
</li>
<li>
Advanced SQL
</li>
<li>
ETL/ELT &amp; Data Pipelines
</li>
<li>
Distributed Systems
</li>
<li>
Cloud/Databricks exposure
</li>
<li>Required Technical Skills1. Apache Spark – Mandatory
Strong hands-on experience with:
<ul>
<li>Apache Spark</li>
<li>Spark Core</li>
<li>Spark SQL</li>
<li>Spark DataFrame</li>
<li>Spark Dataset</li>
<li>Spark Structured Streaming</li>
<li>Spark transformations and actions</li>
<li>RDD concepts</li>
<li>Joins and aggregations</li>
<li>Window functions</li>
<li>Partitioning and repartitioning</li>
<li>Caching and persistence</li>
<li>Broadcast joins</li>
<li>Handling data skew</li>
<li>Spark job optimization</li>
<li>Spark cluster execution and troubleshooting</li>
</ul>
2. Scala – Mandatory
<ul>
<li>Strong programming experience in Scala.</li>
<li>Functional programming concepts.</li>
<li>Collections and higher-order functions.</li>
<li>Case classes, traits, objects, pattern matching.</li>
<li>Exception handling and reusable code development.</li>
<li>Development of Spark applications using Scala.</li>
<li>Ability to write clean, modular, scalable, and maintainable Scala code.</li>
</ul>
3. Apache Kafka – Mandatory
Strong practical experience with:
<ul>
<li>Kafka architecture</li>
<li>Kafka brokers</li>
<li>Topics and partitions</li>
<li>Producers and consumers</li>
<li>Consumer groups</li>
<li>Offsets and offset management</li>
<li>Replication factor</li>
<li>Partition strategy</li>
<li>Message retention</li>
<li>Kafka Producer/Consumer APIs</li>
<li>Kafka Streams / Kafka Connect</li>
<li>Schema Registry</li>
<li>Avro / JSON serialization</li>
<li>Consumer lag monitoring</li>
<li>Kafka troubleshooting</li>
<li>Kafka performance tuning</li>
<li>Integration of Kafka with Spark Structured Streaming</li>
</ul>
4. SQL – Mandatory
Strong SQL skills including:
<ul>
<li>Complex SQL queries</li>
<li>Joins</li>
<li>Subqueries</li>
<li>CTEs</li>
<li>Window functions</li>
<li>Aggregations</li>
<li>Query optimization</li>
<li>Data validation and reconciliation</li>
<li>Stored procedures/functions where applicable</li>
<li>Relational database concepts</li>
</ul>
5. Data Engineering
Strong understanding of:
<ul>
<li>ETL / ELT</li>
<li>Batch processing</li>
<li>Real-time/streaming processing</li>
<li>Data pipelines</li>
<li>Data lakes</li>
<li>Data warehouses</li>
<li>Data modeling</li>
<li>Dimensional modeling</li>
<li>Distributed systems</li>
<li>Data partitioning</li>
<li>Data quality</li>
<li>Data governance</li>
<li>Large-scale data processing</li>
</ul>
Cloud / Big Data Experience
Experience with at least one cloud platform is preferred:
</li>
<li>Required Technical Skills1. Apache Spark – Mandatory
Strong hands-on experience with:
<ul>
<li>Apache Spark</li>
<li>Spark Core</li>
<li>Spark SQL</li>
<li>Spark DataFrame</li>
<li>Spark Dataset</li>
<li>Spark Structured Streaming</li>
<li>Spark transformations and actions</li>
<li>RDD concepts</li>
<li>Joins and aggregations</li>
<li>Window functions</li>
<li>Partitioning and repartitioning</li>
<li>Caching and persistence</li>
<li>Broadcast joins</li>
<li>Handling data skew</li>
<li>Spark job optimization</li>
<li>Spark cluster execution and troubleshooting</li>
</ul>
2. Scala – Mandatory
<ul>
<li>Strong programming experience in Scala.</li>
<li>Functional programming concepts.</li>
<li>Collections and higher-order functions.</li>
<li>Case classes, traits, objects, pattern matching.</li>
<li>Exception handling and reusable code development.</li>
<li>Development of Spark applications using Scala.</li>
<li>Ability to write clean, modular, scalable, and maintainable Scala code.</li>
</ul>
3. Apache Kafka – Mandatory
Strong practical experience with:
<ul>
<li>Kafka architecture</li>
<li>Kafka brokers</li>
<li>Topics and partitions</li>
<li>Producers and consumers</li>
<li>Consumer groups</li>
<li>Offsets and offset management</li>
<li>Replication factor</li>
<li>Partition strategy</li>
<li>Message retention</li>
<li>Kafka Producer/Consumer APIs</li>
<li>Kafka Streams / Kafka Connect</li>
<li>Schema Registry</li>
<li>Avro / JSON serialization</li>
<li>Consumer lag monitoring</li>
<li>Kafka troubleshooting</li>
<li>Kafka performance tuning</li>
<li>Integration of Kafka with Spark Structured Streaming</li>
</ul>
4. SQL – Mandatory
Strong SQL skills including:
<ul>
<li>Complex SQL queries</li>
<li>Joins</li>
<li>Subqueries</li>
<li>CTEs</li>
<li>Window functions</li>
<li>Aggregations</li>
<li>Query optimization</li>
<li>Data validation and reconciliation</li>
<li>Stored procedures/functions where applicable</li>
<li>Relational database concepts</li>
</ul>
5. Data Engineering
Strong understanding of:
<ul>
<li>ETL / ELT</li>
<li>Batch processing</li>
<li>Real-time/streaming processing</li>
<li>Data pipelines</li>
<li>Data lakes</li>
<li>Data warehouses</li>
<li>Data modeling</li>
<li>Dimensional modeling</li>
<li>Distributed systems</li>
<li>Data partitioning</li>
<li>Data quality</li>
<li>Data governance</li>
<li>Large-scale data processing</li>
</ul>
Cloud / Big Data Experience
Experience with at least one cloud platform is preferred:
</li>
<li>Required Technical Skills1. Apache Spark – Mandatory
Strong hands-on experience with:
<ul>
<li>Apache Spark</li>
<li>Spark Core</li>
<li>Spark SQL</li>
<li>Spark DataFrame</li>
<li>Spark Dataset</li>
<li>Spark Structured Streaming</li>
<li>Spark transformations and actions</li>
<li>RDD concepts</li>
<li>Joins and aggregations</li>
<li>Window functions</li>
<li>Partitioning and repartitioning</li>
<li>Caching and persistence</li>
<li>Broadcast joins</li>
<li>Handling data skew</li>
<li>Spark job optimization</li>
<li>Spark cluster execution and troubleshooting</li>
</ul>
2. Scala – Mandatory
<ul>
<li>Strong programming experience in Scala.</li>
<li>Functional programming concepts.</li>
<li>Collections and higher-order functions.</li>
<li>Case classes, traits, objects, pattern matching.</li>
<li>Exception handling and reusable code development.</li>
<li>Development of Spark applications using Scala.</li>
<li>Ability to write clean, modular, scalable, and maintainable Scala code.</li>
</ul>
3. Apache Kafka – Mandatory
Strong practical experience with:
<ul>
<li>Kafka architecture</li>
<li>Kafka brokers</li>
<li>Topics and partitions</li>
<li>Producers and consumers</li>
<li>Consumer groups</li>
<li>Offsets and offset management</li>
<li>Replication factor</li>
<li>Partition strategy</li>
<li>Message retention</li>
<li>Kafka Producer/Consumer APIs</li>
<li>Kafka Streams / Kafka Connect</li>
<li>Schema Registry</li>
<li>Avro / JSON serialization</li>
<li>Consumer lag monitoring</li>
<li>Kafka troubleshooting</li>
<li>Kafka performance tuning</li>
<li>Integration of Kafka with Spark Structured Streaming</li>
</ul>
4. SQL – Mandatory
Strong SQL skills including:
<ul>
<li>Complex SQL queries</li>
<li>Joins</li>
<li>Subqueries</li>
<li>CTEs</li>
<li>Window functions</li>
<li>Aggregations</li>
<li>Query optimization</li>
<li>Data validation and reconciliation</li>
<li>Stored procedures/functions where applicable</li>
<li>Relational database concepts</li>
</ul>
5. Data Engineering
Strong understanding of:
<ul>
<li>ETL / ELT</li>
<li>Batch processing</li>
<li>Real-time/streaming processing</li>
<li>Data pipelines</li>
<li>Data lakes</li>
<li>Data warehouses</li>
<li>Data modeling</li>
<li>Dimensional modeling</li>
<li>Distributed systems</li>
<li>Data partitioning</li>
<li>Data quality</li>
<li>Data governance</li>
<li>Large-scale data processing</li>
</ul>
Cloud / Big Data Experience</li>
<li>
Required Technical Skills1. Apache Spark – Mandatory
Strong hands-on experience with:
<ul>
<li>Apache Spark</li>
<li>Spark Core</li>
<li>Spark SQL</li>
<li>Spark DataFrame</li>
<li>Spark Dataset</li>
<li>Spark Structured Streaming</li>
<li>Spark transformations and actions</li>
<li>RDD concepts</li>
<li>Joins and aggregations</li>
<li>Window functions</li>
<li>Partitioning and repartitioning</li>
<li>Caching and persistence</li>
<li>Broadcast joins</li>
<li>Handling data skew</li>
<li>Spark job optimization</li>
<li>Spark cluster execution and troubleshooting</li>
</ul>
2. Scala – Mandatory
<ul>
<li>Strong programming experience in Scala.</li>
<li>Functional programming concepts.</li>
<li>Collections and higher-order functions.</li>
<li>Case classes, traits, objects, pattern matching.</li>
<li>Exception handling and reusable code development.</li>
<li>Development of Spark applications using Scala.</li>
<li>Ability to write clean, modular, scalable, and maintainable Scala code.</li>
</ul>
3. Apache Kafka – Mandatory
Strong practical experience with:
<ul>
<li>Kafka architecture</li>
<li>Kafka brokers</li>
<li>Topics and partitions</li>
<li>Producers and consumers</li>
<li>Consumer groups</li>
<li>Offsets and offset management</li>
<li>Replication factor</li>
<li>Partition strategy</li>
<li>Message retention</li>
<li>Kafka Producer/Consumer APIs</li>
<li>Kafka Streams / Kafka Connect</li>
<li>Schema Registry</li>
<li>Avro / JSON serialization</li>
<li>Consumer lag monitoring</li>
<li>Kafka troubleshooting</li>
<li>Kafka performance tuning</li>
<li>Integration of Kafka with Spark Structured Streaming</li>
</ul>
4. SQL – Mandatory
Strong SQL skills including:
<ul>
<li>Complex SQL queries</li>
<li>Joins</li>
<li>Subqueries</li>
<li>CTEs</li>
<li>Window functions</li>
<li>Aggregations</li>
<li>Query optimization</li>
<li>Data validation and reconciliation</li>
<li>Stored procedures/functions where applicable</li>
<li>Relational database concepts</li>
</ul>
5. Data Engineering
Strong understanding of:
<ul>
<li>ETL / ELT</li>
<li>Batch processing</li>
<li>Real-time/streaming processing</li>
<li>Data pipelines</li>
<li>Data lakes</li>
<li>Data warehouses</li>
<li>Data modeling</li>
<li>Dimensional modeling</li>
<li>Distributed systems</li>
<li>Data partitioning</li>
<li>Data quality</li>
<li>Data governance</li>
<li>Large-scale data processing</li>
</ul>
Cloud / Big Data Experience
Experience with at least one cloud platform is preferred:
Experience with at least one cloud platform is preferred:
</li>
<li>Required Technical Skills1. Apache Spark – Mandatory
Strong hands-on experience with:
<ul>
<li>Apache Spark</li>
<li>Spark Core</li>
<li>Spark SQL</li>
<li>Spark DataFrame</li>
<li>Spark Dataset</li>
<li>Spark Structured Streaming</li>
<li>Spark transformations and actions</li>
<li>RDD concepts</li>
<li>Joins and aggregations</li>
<li>Window functions</li>
<li>Partitioning and repartitioning</li>
<li>Caching and persistence</li>
<li>Broadcast joins</li>
<li>Handling data skew</li>
<li>Spark job optimization</li>
<li>Spark cluster execution and troubleshooting</li>
</ul>
2. Scala – Mandatory
<ul>
<li>Strong programming experience in Scala.</li>
<li>Functional programming concepts.</li>
<li>Collections and higher-order functions.</li>
<li>Case classes, traits, objects, pattern matching.</li>
<li>Exception handling and reusable code development.</li>
<li>Development of Spark applications using Scala.</li>
<li>Ability to write clean, modular, scalable, and maintainable Scala code.</li>
</ul>
3. Apache Kafka – Mandatory
Strong practical experience with:
<ul>
<li>Kafka architecture</li>
<li>Kafka brokers</li>
<li>Topics and partitions</li>
<li>Producers and consumers</li>
<li>Consumer groups</li>
<li>Offsets and offset management</li>
<li>Replication factor</li>
<li>Partition strategy</li>
<li>Message retention</li>
<li>Kafka Producer/Consumer APIs</li>
<li>Kafka Streams / Kafka Connect</li>
<li>Schema Registry</li>
<li>Avro / JSON serialization</li>
<li>Consumer lag monitoring</li>
<li>Kafka troubleshooting</li>
<li>Kafka performance tuning</li>
<li>Integration of Kafka with Spark Structured Streaming</li>
</ul>
4. SQL – Mandatory
Strong SQL skills including:
<ul>
<li>Complex SQL queries</li>
<li>Joins</li>
<li>Subqueries</li>
<li>CTEs</li>
<li>Window functions</li>
<li>Aggregations</li>
<li>Query optimization</li>
<li>Data validation and reconciliation</li>
<li>Stored procedures/functions where applicable</li>
<li>Relational database concepts</li>
</ul>
5. Data Engineering
Strong understanding of:
<ul>
<li>ETL / ELT</li>
<li>Batch processing</li>
<li>Real-time/streaming processing</li>
<li>Data pipelines</li>
<li>Data lakes</li>
<li>Data warehouses</li>
<li>Data modeling</li>
<li>Dimensional modeling</li>
<li>Distributed systems</li>
<li>Data partitioning</li>
<li>Data quality</li>
<li>Data governance</li>
<li>Large-scale data processing</li>
</ul>
Cloud / Big Data Experience
Experience with at least one cloud platform is preferred:
</li>
<li>Required Technical Skills1. Apache Spark – Mandatory
Strong hands-on experience with:
<ul>
<li>Apache Spark</li>
<li>Spark Core</li>
<li>Spark SQL</li>
<li>Spark DataFrame</li>
<li>Spark Dataset</li>
<li>Spark Structured Streaming</li>
<li>Spark transformations and actions</li>
<li>RDD concepts</li>
<li>Joins and aggregations</li>
<li>Window functions</li>
<li>Partitioning and repartitioning</li>
<li>Caching and persistence</li>
<li>Broadcast joins</li>
<li>Handling data skew</li>
<li>Spark job optimization</li>
<li>Spark cluster execution and troubleshooting</li>
</ul>
2. Scala – Mandatory
<ul>
<li>Strong programming experience in Scala.</li>
<li>Functional programming concepts.</li>
<li>Collections and higher-order functions.</li>
<li>Case classes, traits, objects, pattern matching.</li>
<li>Exception handling and reusable code development.</li>
<li>Development of Spark applications using Scala.</li>
<li>Ability to write clean, modular, scalable, and maintainable Scala code.</li>
</ul>
3. Apache Kafka – Mandatory
Strong practical experience with:
<ul>
<li>Kafka architecture</li>
<li>Kafka brokers</li>
<li>Topics and partitions</li>
<li>Producers and consumers</li>
<li>Consumer groups</li>
<li>Offsets and offset management</li>
<li>Replication factor</li>
<li>Partition strategy</li>
<li>Message retention</li>
<li>Kafka Producer/Consumer APIs</li>
<li>Kafka Streams / Kafka Connect</li>
<li>Schema Registry</li>
<li>Avro / JSON serialization</li>
<li>Consumer lag monitoring</li>
<li>Kafka troubleshooting</li>
<li>Kafka performance tuning</li>
<li>Integration of Kafka with Spark Structured Streaming</li>
</ul>
4. SQL – Mandatory
Strong SQL skills including:
<ul>
<li>Complex SQL queries</li>
<li>Joins</li>
<li>Subqueries</li>
<li>CTEs</li>
<li>Window functions</li>
<li>Aggregations</li>
<li>Query optimization</li>
<li>Data validation and reconciliation</li>
<li>Stored procedures/functions where applicable</li>
<li>Relational database concepts</li>
</ul>
5. Data Engineering
Strong understanding of:
<ul>
<li>ETL / ELT</li>
<li>Batch processing</li>
<li>Real-time/streaming processing</li>
<li>Data pipelines</li>
<li>Data lakes</li>
<li>Data warehouses</li>
<li>Data modeling</li>
<li>Dimensional modeling</li>
<li>Distributed systems</li>
<li>Data partitioning</li>
<li>Data quality</li>
<li>Data governance</li>
<li>Large-scale data processing</li>
</ul>
Cloud / Big Data Experience
Experience with at least one cloud platform is preferred:
</li>
</ul>