Senior Data Engineer (banking Domain)
- Toronto, ON, Canada
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Job details
- Job type
- Contract
- Apply by
- Experience
- · 5+ years with Experience with developing and deploying ETL pipelines using Databricks Pyspark.
- Posting language
- English
- Posted
Original job description
: Senior Data Engineer (banking Domain)
Location: Ottawa/Toronto (Hybrid)
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
More about this job
Responsibilities
Build, optimize, and monitor data pipelines and Databricks Spark workloads that ingest, transform, and process large volumes of data while maintaining data quality, security, and governance. Design enterprise data warehouse solutions and data models, troubleshoot performance issues, and review code for requirements fit and adherence to standards.
Requirements
Requires at least five years of SQL Server development with large datasets and at least five years developing and deploying ETL pipelines using Databricks and PySpark, along with banking-domain and data warehousing experience. Candidates should be proficient in Python and SQL, have cloud data warehouse experience and strong architectural skills, and hold at least a bachelor's degree in engineering or computer science; a master's degree is strongly preferred.
Skills
- SQL
- Data Warehousing
- Banking Domain
- Python
- PySpark
- Databricks
- ETL Pipelines
- Data Integration
- Cloud Data Warehousing
- Dimensional Data Modeling
- Delta Lake
- Streaming Data
- Data Quality
- Data Governance
- Enterprise Data Warehouse Design
- Cloud Data Platform Migration
Visa sponsorship
Not detected in the job text
Categories
- Data & Analytics
- Technology
- Software
- Finance & Accounting
Keywords
- SQL Server
- SQL
- Python
- PySpark
- Databricks
- Apache Spark
- Delta Lake
- Data Warehousing
- Enterprise Data Warehouse
- ETL
- Data Pipelines
- Data Integration
- Cloud Data Warehouse
- Azure Synapse
- BigQuery
- Amazon Redshift
- Snowflake
- OLTP
- OLAP
- Dimensional Modeling
- Facts and Dimensions
- Data Modeling
- Streaming Data
- Structured Data
- Semi-Structured Data
- Unstructured Data
- Data Quality
- Data Security
- Data Governance
- Banking