5–10 years Data Engineering

- Strong Scala programming

- Strong Apache Spark

- Solid Apache Kafka

- Spark Structured Streaming

- Advanced SQL

- ETL/ELT & Data Pipelines

- Distributed Systems

- Cloud/Databricks exposure

- Required Technical Skills1. Apache Spark – Mandatory Strong hands-on experience with:
- Apache Spark
- Spark Core
- Spark SQL
- Spark DataFrame
- Spark Dataset
- Spark Structured Streaming
- Spark transformations and actions
- RDD concepts
- Joins and aggregations
- Window functions
- Partitioning and repartitioning
- Caching and persistence
- Broadcast joins
- Handling data skew
- Spark job optimization
- Spark cluster execution and troubleshooting

2. Scala – Mandatory

- Strong programming experience in Scala.
- Functional programming concepts.
- Collections and higher-order functions.
- Case classes, traits, objects, pattern matching.
- Exception handling and reusable code development.
- Development of Spark applications using Scala.
- Ability to write clean, modular, scalable, and maintainable Scala code.

3. Apache Kafka – Mandatory Strong practical experience with:

- Kafka architecture
- Kafka brokers
- Topics and partitions
- Producers and consumers
- Consumer groups
- Offsets and offset management
- Replication factor
- Partition strategy
- Message retention
- Kafka Producer/Consumer APIs
- Kafka Streams / Kafka Connect
- Schema Registry
- Avro / JSON serialization
- Consumer lag monitoring
- Kafka troubleshooting
- Kafka performance tuning
- Integration of Kafka with Spark Structured Streaming

4. SQL – Mandatory Strong SQL skills including:

- Complex SQL queries
- Joins
- Subqueries
- CTEs
- Window functions
- Aggregations
- Query optimization
- Data validation and reconciliation
- Stored procedures/functions where applicable
- Relational database concepts

5. Data Engineering Strong understanding of:

- ETL / ELT
- Batch processing
- Real-time/streaming processing
- Data pipelines
- Data lakes
- Data warehouses
- Data modeling
- Dimensional modeling
- Distributed systems
- Data partitioning
- Data quality
- Data governance
- Large-scale data processing

Cloud / Big Data Experience Experience with at least one cloud platform is preferred:

- Required Technical Skills1. Apache Spark – Mandatory Strong hands-on experience with:

- Apache Spark
- Spark Core
- Spark SQL
- Spark DataFrame
- Spark Dataset
- Spark Structured Streaming
- Spark transformations and actions
- RDD concepts
- Joins and aggregations
- Window functions
- Partitioning and repartitioning
- Caching and persistence
- Broadcast joins
- Handling data skew
- Spark job optimization
- Spark cluster execution and troubleshooting

2. Scala – Mandatory
- Strong programming experience in Scala.
- Functional programming concepts.
- Collections and higher-order functions.
- Case classes, traits, objects, pattern matching.
- Exception handling and reusable code development.
- Development of Spark applications using Scala.
- Ability to write clean, modular, scalable, and maintainable Scala code.

3. Apache Kafka – Mandatory Strong practical experience with:
- Kafka architecture
- Kafka brokers
- Topics and partitions
- Producers and consumers
- Consumer groups
- Offsets and offset management
- Replication factor
- Partition strategy
- Message retention
- Kafka Producer/Consumer APIs
- Kafka Streams / Kafka Connect
- Schema Registry
- Avro / JSON serialization
- Consumer lag monitoring
- Kafka troubleshooting
- Kafka performance tuning
- Integration of Kafka with Spark Structured Streaming

4. SQL – Mandatory Strong SQL skills including:
- Complex SQL queries
- Joins
- Subqueries
- CTEs
- Window functions
- Aggregations
- Query optimization
- Data validation and reconciliation
- Stored procedures/functions where applicable
- Relational database concepts

5. Data Engineering Strong understanding of:
- ETL / ELT
- Batch processing
- Real-time/streaming processing
- Data pipelines
- Data lakes
- Data warehouses
- Data modeling
- Dimensional modeling
- Distributed systems
- Data partitioning
- Data quality
- Data governance
- Large-scale data processing

Cloud / Big Data Experience Experience with at least one cloud platform is preferred:





- Required Technical Skills1. Apache Spark – Mandatory Strong hands-on experience with:

- Apache Spark
- Spark Core
- Spark SQL
- Spark DataFrame
- Spark Dataset
- Spark Structured Streaming
- Spark transformations and actions
- RDD concepts
- Joins and aggregations
- Window functions
- Partitioning and repartitioning
- Caching and persistence
- Broadcast joins
- Handling data skew
- Spark job optimization
- Spark cluster execution and troubleshooting

2. Scala – Mandatory
- Strong programming experience in Scala.
- Functional programming concepts.
- Collections and higher-order functions.
- Case classes, traits, objects, pattern matching.
- Exception handling and reusable code development.
- Development of Spark applications using Scala.
- Ability to write clean, modular, scalable, and maintainable Scala code.

3. Apache Kafka – Mandatory Strong practical experience with:
- Kafka architecture
- Kafka brokers
- Topics and partitions
- Producers and consumers
- Consumer groups
- Offsets and offset management
- Replication factor
- Partition strategy
- Message retention
- Kafka Producer/Consumer APIs
- Kafka Streams / Kafka Connect
- Schema Registry
- Avro / JSON serialization
- Consumer lag monitoring
- Kafka troubleshooting
- Kafka performance tuning
- Integration of Kafka with Spark Structured Streaming

4. SQL – Mandatory Strong SQL skills including:
- Complex SQL queries
- Joins
- Subqueries
- CTEs
- Window functions
- Aggregations
- Query optimization
- Data validation and reconciliation
- Stored procedures/functions where applicable
- Relational database concepts

5. Data Engineering Strong understanding of:
- ETL / ELT
- Batch processing
- Real-time/streaming processing
- Data pipelines
- Data lakes
- Data warehouses
- Data modeling
- Dimensional modeling
- Distributed systems
- Data partitioning
- Data quality
- Data governance
- Large-scale data processing

Cloud / Big Data Experience
- Required Technical Skills1. Apache Spark – Mandatory

Strong hands-on experience with:

- Apache Spark
- Spark Core
- Spark SQL
- Spark DataFrame
- Spark Dataset
- Spark Structured Streaming
- Spark transformations and actions
- RDD concepts
- Joins and aggregations
- Window functions
- Partitioning and repartitioning
- Caching and persistence
- Broadcast joins
- Handling data skew
- Spark job optimization
- Spark cluster execution and troubleshooting

2. Scala – Mandatory
- Strong programming experience in Scala.
- Functional programming concepts.
- Collections and higher-order functions.
- Case classes, traits, objects, pattern matching.
- Exception handling and reusable code development.
- Development of Spark applications using Scala.
- Ability to write clean, modular, scalable, and maintainable Scala code.

3. Apache Kafka – Mandatory

Strong practical experience with

- Kafka architecture
- Kafka brokers
- Topics and partitions
- Producers and consumers
- Consumer groups
- Offsets and offset management
- Replication factor
- Partition strategy
- Message retention
- Kafka Producer/Consumer APIs
- Kafka Streams / Kafka Connect
- Schema Registry
- Avro / JSON serialization
- Consumer lag monitoring
- Kafka troubleshooting
- Kafka performance tuning
- Integration of Kafka with Spark Structured Streaming

4. SQL – Mandatory

Strong SQL skills including

- Complex SQL queries
- Joins
- Subqueries
- CTEs
- Window functions
- Aggregations
- Query optimization
- Data validation and reconciliation
- Stored procedures/functions where applicable
- Relational database concepts

5. Data Engineering

Strong understanding of

- ETL / ELT
- Batch processing
- Real-time/streaming processing
- Data pipelines
- Data lakes
- Data warehouses
- Data modeling
- Dimensional modeling
- Distributed systems
- Data partitioning
- Data quality




- Data governance
- Large-scale data processing

Cloud / Big Data Experience

Experience with at least one cloud platform is preferred:

Experience with at least one cloud platform is preferred:

- Required Technical Skills1. Apache Spark – Mandatory Strong hands-on experience with:

- Apache Spark
- Spark Core
- Spark SQL
- Spark DataFrame
- Spark Dataset
- Spark Structured Streaming
- Spark transformations and actions
- RDD concepts
- Joins and aggregations
- Window functions
- Partitioning and repartitioning
- Caching and persistence
- Broadcast joins
- Handling data skew
- Spark job optimization
- Spark cluster execution and troubleshooting

2. Scala – Mandatory
- Strong programming experience in Scala.
- Functional programming concepts.
- Collections and higher-order functions.
- Case classes, traits, objects, pattern matching.
- Exception handling and reusable code development.
- Development of Spark applications using Scala.
- Ability to write clean, modular, scalable, and maintainable Scala code.

3. Apache Kafka – Mandatory Strong practical experience with:
- Kafka architecture
- Kafka brokers
- Topics and partitions
- Producers and consumers
- Consumer groups
- Offsets and offset management
- Replication factor
- Partition strategy
- Message retention
- Kafka Producer/Consumer APIs
- Kafka Streams / Kafka Connect
- Schema Registry
- Avro / JSON serialization
- Consumer lag monitoring
- Kafka troubleshooting
- Kafka performance tuning
- Integration of Kafka with Spark Structured Streaming

4. SQL – Mandatory Solid SQL skills including:
- Complex SQL queries
- Joins
- Subqueries
- CTEs
- Window functions
- Aggregations
- Query optimization
- Data validation and reconciliation
- Stored procedures/functions where applicable
- Relational database concepts

5. Data Engineering Strong understanding of:
- ETL / ELT
- Batch processing
- Real-time/streaming processing
- Data pipelines
- Data lakes
- Data warehouses
- Data modeling
- Dimensional modeling
- Distributed systems
- Data partitioning
- Data quality
- Data governance
- Large-scale data processing

Cloud / Big Data Experience Experience with at least one cloud platform is preferred:

- Required Technical Skills1. Apache Spark – Mandatory Strong hands-on experience with:

- Apache Spark
- Spark Core
- Spark SQL
- Spark DataFrame
- Spark Dataset
- Spark Structured Streaming
- Spark transformations and actions
- RDD concepts
- Joins and aggregations
- Window functions
- Partitioning and repartitioning
- Caching and persistence
- Broadcast joins
- Handling data skew
- Spark job optimization
- Spark cluster execution and troubleshooting

2. Scala – Mandatory
- Strong programming experience in Scala.
- Functional programming concepts.
- Collections and higher-order functions.
- Case classes, traits, objects, pattern matching.
- Exception handling and reusable code development.
- Development of Spark applications using Scala.
- Ability to write clean, modular, scalable, and maintainable Scala code.

3. Apache Kafka – Mandatory Strong practical experience with:
- Kafka architecture
- Kafka brokers
- Topics and partitions
- Producers and consumers
- Consumer groups
- Offsets and offset management
- Replication factor
- Partition strategy
- Message retention
- Kafka Producer/Consumer APIs
- Kafka Streams / Kafka Connect
- Schema Registry
- Avro / JSON serialization
- Consumer lag monitoring
- Kafka troubleshooting
- Kafka performance tuning
- Integration of Kafka with Spark Structured Streaming

4. SQL – Mandatory Strong SQL skills including:
- Complex SQL queries
- Joins
- Subqueries
- CTEs
- Window functions
- Aggregations
- Query optimization
- Data validation and reconciliation
- Stored procedures/functions where applicable
- Relational database concepts

5. Data Engineering Strong understanding of:
- ETL / ELT
- Batch processing
- Real-time/streaming processing
- Data pipelines
- Data lakes
- Data warehouses
- Data modeling
- Dimensional modeling
- Distributed systems
- Data partitioning
- Data quality
- Data governance
- Large-scale data processing

Cloud / Big Data Experience Experience with at least one cloud platform is preferred:

📌 DE-Spark,Scala,kafka (Bengaluru)
🏢 Zorba Consulting India
📍 Bengaluru