Role: Data Engineer

Hybrid: 3 days a week in office

Location: Toronto

Job Summary An experienced Data Engineer with strong expertise in Big Data technologies to design, develop, and support enterprise-scale data platforms. The ideal candidate should possess hands-on experience in PySpark, Apache Spark, Kafka, Hadoop ecosystem components, and Apache NiFi, with a strong understanding of data ingestion, transformation, and real-time processing frameworks.

Key Responsibilities

Design, develop, and optimize scalable data pipelines using PySpark, Spark, Hadoop, and Apache NiFi.
Build and maintain batch and real-time data processing solutions.
Develop and support Kafka-based streaming applications and event-driven architectures.
Create and optimize ETL/ELT workflows for large-scale structured and unstructured datasets.
Develop complex SQL queries for data extraction, transformation, validation, and troubleshooting.
Implement data ingestion solutions from databases, APIs, files, and streaming sources.
Monitor, troubleshoot, and enhance the performance of Spark jobs and data pipelines.
Collaborate with architects, business analysts, and development teams to deliver high-quality data solutions.
Support platform upgrades, deployments, testing, certification, and production releases.
Ensure data quality, governance, security, and operational excellence across data platforms.

Mandatory Skills

PySpark
Apache Spark (Spark SQL, DataFrames)
Apache Kafka
Hadoop Ecosystem (HDFS, Hive, YARN)
Apache NiFi
SQL
Python

Preferred Skills

Spark Streaming
Airflow / Oozie
Hive
Scala
Jenkins, Bitbucket, Git
JIRA, Confluence
Cloud Platforms (GCP/AWS/Azure)
Data Warehousing concepts and Dimensional Modeling

Data Engineer

Job description