Role: Data Engineer

Location: Toronto (Hybrid)

Experience: 8+ years

Type: Contract

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 solid 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 / Toronto (Hybrid) - Contract
🏢 AceStack
📍 Toronto