Loopio is hiring a Data Engineer in Vancouver!
Loopio, a Canadian software company, is hiring an experienced Data Engineer to join its Data Engineering team, which is focused on speeding up and simplifying the RFP response process for customers. The role centres on building, scaling, and maintaining the pipelines and platforms that power analytics — both for internal teams and for data embedded directly in Loopio's product. The hire will work alongside ML engineers, architects, data scientists, product managers, and business stakeholders, helping the company get more value from its data and enabling data-driven decisions across the organization. Day to day, this means integrating sources such as clickstream, relational, and unstructured data into a data lake, maintaining a feature store used for analytics and modeling, and building automation for orchestration, testing, monitoring, and data operations. The stack leans heavily on Databricks and AWS (Redshift, S3, RDS), with workflow tooling for orchestration. Loopio describes itself as remote-first and hires within hub regions; this posting is tied to Vancouver and British Columbia, with optional co-working space available and virtual collaboration across teams in Canada, the UK, and India using core sync hours. Candidates with three or more years in data engineering or a comparable role are encouraged to apply, including those who don't meet every listed criterion — the company explicitly invites applications from people with non-traditional backgrounds and career transitions.
Key responsibilities
- Build, evolve, and scale data platforms and ETL pipelines with reliability and business growth in mind
- Integrate clickstream, relational, and unstructured data sources into the company data lake
- Develop and maintain a feature store supporting analytics and machine learning modeling
- Create automation tooling for data orchestration, evaluation, testing, monitoring, and operations
- Build scalable pipelines on Databricks and AWS services such as Redshift, S3, and RDS
- Support and maintain the data warehouse in Redshift and the Databricks delta lake
- Orchestrate workflows using frameworks such as Databricks Jobs or Airflow
- Partner with data scientists on predictive models used in the product and by internal teams like RevOps, Marketing, and CX
- Work with stakeholders to translate business data needs into processes and clearer decision-making
- Promote data literacy and data-driven decision-making across the organization
- Communicate technical roadmaps, risks, and mitigation plans clearly to varied audiences
Key requirements
- 3+ years in a data engineering or similar role
- Strong grasp of database concepts, data modeling, SQL, and query optimization
- Programming experience in Python (preferred), Scala, or Java for data manipulation
- Hands-on AWS experience across RDS, S3, Redshift, Glue, QuickSight, Athena, and ECS
- Solid understanding of relational databases (RDS/Aurora) and NoSQL engines such as Redis, DynamoDB, or Neptune
- ETL and data warehousing experience, including Inmon, Kimball, or Data Vault models
- Experience with MPP frameworks such as Spark or Flink
- Familiarity with CI/CD tools like Jenkins and orchestration tools like Databricks Jobs or Airflow
- Experience with BI and visualization platforms such as QuickSight, Tableau, or Sisense
- Exposure to clickstream data tools such as Amplitude or Pendo
- Background in a high-growth agile software environment and supporting large-scale production systems
- Strong communication, collaboration, and analytical skills
- Seniority: Experienced (3+ years)
- Work mode: Remote-first within Canada; Vancouver/BC hub with optional co-working
- Key skills: Python, SQL, ETL, Data Modeling, AWS, Redshift, S3, Databricks, Spark, Airflow, Jenkins, CI/CD, Data Warehousing, NoSQL, QuickSight
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