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)

- Robust 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

📌 Spark Scala Developer (Calgary)
🏢 Veridian Tech Solutions
📍 Calgary