Skills: Strong focus on ETL, SQL, SSIS, Tidal, Airflow
Job Description:
This position is for an Integration engineer with a background in SSIS, Python, 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:
Provide Level 2/3 support of existing ETL processes.
Monitor and troubleshoot scheduled ETL jobs, identify and resolve issues or bottlenecks.
Proficient in developing ETL processes in SSIS, Python and SQL (SQL Server / Oracle)
Proficient writing SQL queries and programming including stored procedures and reverse engineering existing process.
Build and optimize data pipelines for efficient data ingestion, transformation and loading from various sources while ensuring data quality and integrity.
Working with structured, semi structured and unstructured data.
Implement best practices for data management, security, and governance.
Perform code reviews to ensure fit to requirements, optimal execution patterns and adherence to established standards.
Required Skills:
5+ years SSIS development experience
5+ years Python coding experience.
5+ years SQL Server based development of large datasets
Additional Skills:
Experience in Financial Services industry
Experience with Enterprise Schedulers / Orchestration platforms such as Tidal, Airflow.
Experience in any cloud data warehouse like Snowflake.
Experience in Data warehousing - OLTP, Dimensions, Facts, and Data modeling.
Experience with Cloud based data architectures, messaging, and analytics.
Experience with developing and deploying ETL pipelines using Databricks Pyspark.
Education:
Minimally a BA/BS degree within an engineering and/or computer science discipline
Master's degree preferred
Integration Engineer
Location: Remote (open for Canada only)
Contract
Skills: Strong focus on ETL, SQL, SSIS, Tidal, Airflow
Job Description:
This position is for an Integration engineer with a background in SSIS, Python, 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:
Provide Level 2/3 support of existing ETL processes.
Monitor and troubleshoot scheduled ETL jobs, identify and resolve issues or bottlenecks.
Proficient in developing ETL processes in SSIS, Python and SQL (SQL Server / Oracle)
Proficient writing SQL queries and programming including stored procedures and reverse engineering existing process.
Build and optimize data pipelines for efficient data ingestion, transformation and loading from various sources while ensuring data quality and integrity.
Working with structured, semi structured and unstructured data.
Implement best practices for data management, security, and governance.
Perform code reviews to ensure fit to requirements, optimal execution patterns and adherence to established standards.
Required Skills:
5+ years SSIS development experience
5+ years Python coding experience.
5+ years SQL Server based development of large datasets
Additional Skills:
Experience in Financial Services industry
Experience with Enterprise Schedulers / Orchestration platforms such as Tidal, Airflow.
Experience in any cloud data warehouse like Snowflake.
Experience in Data warehousing - OLTP, Dimensions, Facts, and Data modeling.
Experience with Cloud based data architectures, messaging, and analytics.
Experience with developing and deploying ETL pipelines using Databricks Pyspark.
Education:
Minimally a BA/BS degree within an engineering and/or computer science discipline
Master's degree preferred
What You'll Do
Provide Level 2/3 support of existing ETL processes.
Monitor and troubleshoot scheduled ETL jobs, identify and resolve issues or bottlenecks.
Proficient in developing ETL processes in SSIS, Python and SQL (SQL Server / Oracle)
Proficient writing SQL queries and programming including stored procedures and reverse engineering existing process.
Build and optimize data pipelines for efficient data ingestion, transformation and loading from various sources while ensuring data quality and integrity.
Working with structured, semi structured and unstructured data.
