Data Engineer

Job summary

Build, optimize, and monitor data pipelines and Databricks Spark workloads that ingest, transform, and analyze large datasets while maintaining data quality, security, and governance. Design and develop enterprise data warehouse solutions, write and review SQL and Python code, troubleshoot performance issues, and collaborate with business users.

Job details

Role: Senior Data Engineer Location: Ottawa/Toronto (Hybrid) Duration: Contract Must have : SQL+ Datawarehouse+ Banking domain Job Description: This position is for a Senior Data engineer with a background in Python, Pyspark, SQL and data warehousing for enterprise level systems. The position calls for someone that is comfortable working with business users along with business analyst expertise. Major Responsibilities: · Build and optimize data pipelines for efficient data ingestion, transformation and loading from various sources while ensuring data quality and integrity. · Design, develop, and deploy Spark program in databricks environment to process and analyze large volumes of data. · Experience of Delta Lake, DWH, Data Integration, Cloud, Design and Data Modelling. · Proficient in developing programs in Python and SQL · Experience with Data warehouse Dimensional data modeling. · Working with event based/streaming technologies to ingest and process data. · Working with structured, semi structured and unstructured data. · Optimize Databricks jobs for performance and scalability to handle big data workloads. · Monitor and troubleshoot Databricks jobs, identify and resolve issues or bottlenecks. · Implement best practices for data management, security, and governance within the Databricks environment. Experience designing and developing Enterprise Data Warehouse solutions. · Proficient writing SQL queries and programming including stored procedures and reverse engineering existing process. · Perform code reviews to ensure fit to requirements, optimal execution patterns and adherence to established standards. Skills: · 5+ years - SQL Server based development of large datasets · 5+ years with Experience with developing and deploying ETL pipelines using Databricks Pyspark. · Experience in any cloud data warehouse like Synapse, Big Query, Redshift, Snowflake. · Experience in Data warehousing - OLTP, OLAP, Dimensions, Facts, and Data modeling. · Previous experience leading an enterprise-wide Cloud Data Platform migration with strong architectural and design skills. Education • Minimally a BA degree within an engineering and/or computer science discipline • Master’s degree strongly preferred

What you’ll do

Build, optimize, and monitor data pipelines and Databricks Spark workloads that ingest, transform, and analyze large datasets while maintaining data quality, security, and governance. Design and develop enterprise data warehouse solutions, write and review SQL and Python code, troubleshoot performance issues, and collaborate with business users.

Requirements

Requires at least five years of SQL Server development with large datasets and experience developing and deploying ETL pipelines using Databricks and PySpark. Candidates should have data warehousing and dimensional modeling expertise, cloud data warehouse experience, strong architectural and design skills, and a bachelor's degree in engineering or computer science; a master's degree is strongly preferred.

Listed skills

• SQL · Preferred
• Python · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• SQL
• SQL Server
• Python
• PySpark
• Databricks
• Data Warehousing
• ETL Pipelines
• Data Modeling
• Dimensional Modeling
• Delta Lake
• Data Integration
• Cloud Data Platforms
• Streaming Data
• Data Quality
• Data Governance
• Banking Domain

Job areas

• Data & Analytics
• Technology
• Software
• Finance & Accounting

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