Our Enterprise Data & Analytics (EDA) is looking for a talented and experienced Senior Data Engineer to join our growing curation & engineering team. You’ll work in a collaborative Agile environment using the latest in engineering best practices with involvement in all aspects of the software development lifecycle. You will be responsible for designing & developing curated data products, applying standard architectural practices supporting Data Product Managers in evolving our core data products. You will primarily develop on Google Cloud Platform and other technologies such as BigQuery, Airflow, Python, SQL, Linux, Docker and Kubernetes.
What you get to do every single day:
Create and maintain data pipelines and data products in the Enterprise Data Warehouse
Identify, design, and implement internal process improvements: automating manual processes, optimising data delivery
Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and cloud native technologies
Build analytics solutions that provide practical insights into customer, finance, operations, and other key business domains
Work with data and analytics experts to strive for greater functionality in our data systems
What you bring to the role:
5+ years of data engineering experience building, working & maintaining data pipelines on big data environments
Familiarity with processes supporting data transformation, data structures, metadata, dependency and workload management.
Advanced working SQL knowledge and experience working with relational databases, query authoring (SQL) as well as working familiarity with a variety of databases.
Experience performing root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement.
Good command of Python and SQL
What the stack looks like:
Our platform runs on GCP and AWS, fully orchestrated by Kubernetes
We use Terraform and helm to deploy and manage platform components.
Our data pipeline is built on Kafka, GCS, BigQuery, SQL and Airflow
And the underlying code written in Python and Go
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