Spark Scala Developer

Required Qualifications: At least 4 years of Information Technology experience 4+ years of experience in Big Data technologies. Strong expertise in: Apache Spark (Core, SQL, DataFrames, RDDs) Scala programming PySpark Hands-on experience with: Kafka (real-time streaming) Hadoop ecosystem (HDFS, Hive, Impala) NoSQL Databases (HBase, MongoDB, Couchbase) Strong understanding of distributed computing concepts and data processing frameworks. Experience in building ETL/data pipelines for large-scale datasets. Proficiency in SQL and data modeling. Preferred Qualifications: Hands-on experience with data lakes, data warehouses, and scalable ETL pipeline design, including batch and real-time processing architecture. Strong understanding and practical exposure to Agile software development methodologies (Scrum) and SDLC practices. Proven experience in Banking domain, supporting use cases such as fraud detection, risk analytics, regulatory reporting, and customer insights. Excellent analytical, problem-solving, and communication skills, with the ability to translate business requirements into scalable technical solutions. Demonstrated ability to work effectively in cross-functional, multi-stakeholder environments, collaborating with Business, Data Engineering, and Architecture teams. Experience with real-time data streaming frameworks such as Kafka and Spark Streaming for low-latency processing. Understanding data modeling concepts (dimensional modeling, snowflake schemas) to support analytics workloads. Experience and desire to work in a global delivery environment. Key Responsibilities: Design and develop large-scale data processing pipelines using Apache Spark (Scala & PySpark) Build and optimize batch and real-time data processing workflows using Spark, Kafka, and Hadoop ecosystem Develop Spark applications using RDDs, DataFrames, and Spark SQL for complex transformations Develop and optimize PySpark applications leveraging joins, Spark DAG execution flow, stage optimization, transformation techniques, and streaming with dynamic allocation and failover handling. Implement streaming pipelines using Kafka and Spark Streaming / Structured Streaming Develop and maintain HDFS, Hive, NoSql and Impala-based data lake solutions Convert existing SQL/Hive workloads into optimized Spark jobs for improved performance Work with ETL pipelines to ingest, cleanse, transform, and process large datasets Optimize performance through partitioning, caching, serialization, and tuning techniques Handle data formats such as Parquet, ORC, Avro, JSON Integrate multiple data sources including streaming systems, flat files RDBMS, and APIs Collaborate with cross-functional teams to understand business requirements and translate them into scalable technical solutions Ensure data quality, reliability, and performance monitoring across pipelines Participate in code reviews, design discussions, and best practices implementation

Design and develop large-scale data processing pipelines using Apache Spark, Scala, and PySpark. Build and optimize batch and real-time workflows integrating Kafka and the Hadoop ecosystem.

Requires at least 4 years of IT experience with strong expertise in Big Data technologies and distributed computing. Proficiency in SQL, NoSQL databases, and experience with ETL pipeline design is essential.

• MongoDBPreferred
• SQLPreferred

Identified from the job description. Confirm important requirements above.

• Apache Spark
• Scala
• PySpark
• Kafka
• Hadoop
• HDFS
• Hive
• Impala
• NoSQL
• HBase
• MongoDB
• Couchbase
• SQL
• ETL Pipelines
• Data Modeling
• Distributed Computing

• Data & Analytics
• Software
• Technology
• Engineering
• Finance & Accounting

Minimum experience

5+ years

Apply by

Sep 10, 2026

Posting language

English

Working hours

40 hours per week

Seniority

Mid-Senior level

Application method

Direct apply is available