JOB PROFILE SENIOR DATA ENGINEER
We are looking for experienced Data Engineer to join our growing team of analytics experts who has attained a Graduate degree in Computer Science, Statistics, Informatics, Information Systems or another quantitative field.
The Data Engineer will support our software developers, database architects, data analysts and data scientists on data initiatives and will ensure optimal data delivery architecture is consistent throughout ongoing projects. They must be self-directed and comfortable supporting the data needs of multiple systems and products. The right candidate will be excited by the prospect of optimizing or even re-designing our company’s data architecture to support our next generation of products and data initiatives.
Responsibilities for Data Engineer
· Having strong Python scripting experience
· Extensive experience connecting to various data sources and structures: APIs, NoSQL, RDBMS, Blob Storage, Data Lake, etc.
· Deep understanding of ETL, ELT, data ingestion/cleansing and engineering skills
· Having experience building data ingestion pipelines using Azure Data Factory and SSIS to ingest structured and unstructured data with C# programming experience.
· Having strong knowledge on Azure Storage schematics such as Gen1 and Gen2
· Have experience on CDC tools like Transaction Replication, Debezium or HVR.
· Having knowledge of Azure networking, security, key vaults, etc.
· Advanced Analytics including Azure Data Bricks, visualization tools as PowerBI, Tableau
· Have experience in Data Governance, Data Catalog, Master Data Management
· Big Data implementation - using Open Source and Non-SQL technologies such as Databricks, Spark, Spark Streaming, Kafka, Cosmos DB, Snowflake and Python.
· Knowledge of Lambda and Kappa architecture patterns.
· Knowledge of Master Data Management (MDM) and Data Quality tools and processes.
· Experience with messaging/eventing architecture concepts and technology such as, Kafka, Event Hubs, Streams
· Strong analytic skills related to working with unstructured datasets.
· Build processes supporting data transformation, data structures, metadata, dependency and workload management.
· A successful history of manipulating, processing and extracting value from large disconnected datasets.