Responsibilities
Data Modelling and Extract, Transform and Load (ETL):
Design and implement robust data models to support
analytics and reporting requirements.
Develop and maintain scalable ETL processes from
various sources, including multiple ERP systems, into a data warehouse.
Data Ingestion and Automation data pipelines
Implement data validation and quality checks to
ensure accuracy and completeness.
Design, build, and maintain automated data pipelines
to streamline data processing and transformation.
Utilize orchestration tools to schedule and monitor
pipeline workflows.
Collaborate with data analysts to understand data
requirements and support their analysis needs.
Optimize data structures and queries to enhance
performance and usability for analytical purposes.
Data Warehousing
Design and optimize data warehousing solutions to
support business intelligence and analytics needs.
Implement data modelling techniques to organize and
structure data for optimal querying and analysis.
Optimization and Performance Tuning of Data
Dashboards
Troubleshooting and fixing issues on existing
reports/dashboards while also continuously building improvements.
Optimize dashboard performance and ensure
responsiveness for large datasets and complex queries.
Design, Data Visualization and Dashboards
Experience:
5+ years of industry experience working on data
engineering with a focus on data ingestion, data warehousing, pipeline
automation, and ETL development
Experience building infrastructure to support
streaming or offline data.
Extensive programming experience in
Python/Scala/Java
Experience with SQL in addition to one or more of
Spark/Hadoop/Hive/HDFS
Working knowledge of databases, data systems, and
analytics solutions, including proficiency in SQL, NoSQL, Java, Spark and
Amazon Redshift for reporting and dashboard building.
Experience with implementing unit and integration
testing.
Ability to gather requirements and communicate with
stakeholders across data, software, and platform teams.
Ability to develop a strategic vision for data
pipelining and infrastructure.
Experience managing a team of mid-level
engineers.
Sense of adventure and willingness to dive in, think
big, and execute with a team
Qualifications:
Bachelors or master’s in computer science, machine
learning, or related field
Language(s):
English
French is a plus.
Technology:
Python, Java, SQL, NoSQL, Amazon Redshift, Kafka,
Apache Beam, Apache Airflow, Apache Spark
How To Apply
