Technology Lead - PySpark Developer

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.

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.

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

Distributed Data Processing.

Spark Optimization & Performance Tuning.

Real-time Data Streaming.

Data Modeling & ETL Design.

Problem-solving and Analytical Thinking.

Strong Communication & Stakeholder Management.

Exposure to Machine Learning pipelines or MLOps workflows.

Experience with Databricks platform.

Experience with AWS/GCP.