Spark Scala Developer / Big data engineer

Hi,

Hope you are doing good!

We have a Full-time opportunity for you as Spark Scala Developer @ Mississauga, ON

Role: Spark Scala Developer

Locations: Mississauga, ON

Type of Hiring: FTE

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, Data Frames, 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

Key Skills:

• Distributed Data Processing.
• Spark Optimization & Performance Tuning.
• Real-time Data Streaming.
• Data Modeling & ETL Design.
• Problem-solving and Analytical Thinking.
• Strong Communication & Stakeholder Management.

Nice to Have:

• Exposure to Machine Learning pipelines or MLOps workflows.
• Experience with Databricks platform.
• Experience with AWS/GCP.