About the Role
A Data Engineer at SafetyCulture is responsible for empowering the Data Analytics team with the right data model for their needs, enabling them to find insights that can make a change to SafetyCulture as a company. This is a key role in keeping the organization's knowledge organized, easily accessible, and accurate.
As a Data Engineer you collaborate with different stakeholders across the business (Finance/Marketing/Sales) to turn billions of rows of source data into highly valuable datasets and data products. SafetyCulture is expanding rapidly which requires the Data Engineer to integrate different systems and datasets from differing business contexts.
What You’ll Be Doing
- Deliver big data datasets on time and with quality using the most out of our infrastructure.
- Support the design of new feature data tracking to match our patterns.
- Apply software engineering principles to our data pipeline, providing reusable and efficient code in Python, Scala and SQL.
- Support our infrastructure in AWS and Databricks using Terraform.
- Manage our data architecture via orchestration tools like Airflow.
- Manage and optimise data at scale with Data Lake (Delta), Data Warehouse (Redshift and Databricks), ML architecture and external tools (Fivetran and Segment).
- Work closely with the Software Engineers to design efficient data contracts, ensuring the requirements of data analysts, and support the analytics engineer team with sourcing very reliable datasets.
- Contribute to fostering an open, positive atmosphere within the team, promoting collaboration, learning, and growth.
What you will need to be successful?
- Experience with data engineering architecture and cloud base solutions for big data.
- Python or Scala - Comfort of writing solutions for scalable pipelines in Apache Spark.
- Data Orchestration tools such as Apache Airflow.
- AWS Infrastructure Experience (Terraform, EMR, S3, Glue).
- Data warehouse experience (Redshift, Databricks or Snowflake).
- Curiosity and self-drive to continuously learn new techniques and tools to extract value from data.
- High level of comfort with ambiguity and ability to work in a fast-paced, agile environment in which you rapidly adapt and learn for any given business problem.