Data Engineer - Platform Central Data
Spotify
Data Engineer - Platform Central Data
Job Description
We’re looking for a mid-level Data Engineer to build data-driven engineering initiatives within Spotify’s Platform Mission on the Platform Central Data (PCD) squad, a mixed Data Engineering/Analytics Engineering team. You’ll design and operate reliable datasets and workflow automations that power developer productivity, platform health, leadership decision-making, and ML and AI Platform Metrics. Working shoulder to shoulder with our AE teammates, plus Product and Platform partners, you’ll turn complex platform signals into trusted, actionable data that helps Spotify ship faster and safer.
Locations
- Toronto, Canada
Salary
Salary details available upon request
Estimated Salary Rangemedium confidence
110,000 - 165,000 CAD / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- SQLintermediate
- Pythonintermediate
- Scalaintermediate
- Apache Sparkintermediate
- Sciointermediate
- Apache Beamintermediate
- Flinkintermediate
- BigQueryintermediate
- Snowflakeintermediate
- Redshiftintermediate
- Databricks SQLintermediate
- dbtintermediate
- Airflowintermediate
- Dagsterintermediate
- Prefectintermediate
- Flyteintermediate
- CI/CDintermediate
- observabilityintermediate
- testingintermediate
- monitoringintermediate
- communicationintermediate
- prioritizationintermediate
Required Qualifications
- 3+ years building production quality data solutions with complex domain logic, and use strong SQL to answer questions and debug problems (experience)
- Experience with a cloud data warehouse, for example BigQuery, Snowflake, Redshift, or Databricks SQL (experience)
- Experience with distributed processing frameworks such as Apache Spark or Scio (Scala for Apache Beam), or equivalents like Apache Beam or Flink (experience)
- Comfortable with Python or Scala and with one or more transformation frameworks, for example dbt or equivalent SQL based transformations (experience)
- Used a workflow orchestrator such as Airflow, Dagster, Prefect, or Flyte, and care about reliability, monitoring, and testability (experience)
- Communicate clearly with both technical and non technical partners, and can prioritize and deliver in a changing environment (experience)
Preferred Qualifications
- Experience in platform and developer productivity, experimentation, or ML and AI platform metrics (experience)
Responsibilities
- Build and operate reliable, well-modeled data pipelines and products using SQL, Python, and Scala, with strong testing, observability, and CI/CD. For distributed processing you will use Apache Spark or Scio, or equivalent technologies like Apache Beam or Flink
- Partner in a mixed DE and AE squad to translate complex platform and developer experience use cases into trustworthy datasets, metrics, and ML and AI insights workflows
- Improve data quality, performance, and cost efficiency across existing pipelines, including troubleshooting, backfills, and iterative hardening
- Collaborate with Product, Engineering, and Data Science partners to deliver end to end outcomes, from scoping and modeling to rollout and documentation
- Contribute to agile rituals and take part in a fair support rotation for key pipelines and datasets
Benefits
- general: Flexibility to work where you work best! There will be some in person meetings, but still allows for flexibility to work from home
Tags & Categories
Data Engineer - Platform Central Data
Spotify
Data Engineer - Platform Central Data
Job Description
We’re looking for a mid-level Data Engineer to build data-driven engineering initiatives within Spotify’s Platform Mission on the Platform Central Data (PCD) squad, a mixed Data Engineering/Analytics Engineering team. You’ll design and operate reliable datasets and workflow automations that power developer productivity, platform health, leadership decision-making, and ML and AI Platform Metrics. Working shoulder to shoulder with our AE teammates, plus Product and Platform partners, you’ll turn complex platform signals into trusted, actionable data that helps Spotify ship faster and safer.
Locations
- Toronto, Canada
Salary
Salary details available upon request
Estimated Salary Rangemedium confidence
110,000 - 165,000 CAD / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- SQLintermediate
- Pythonintermediate
- Scalaintermediate
- Apache Sparkintermediate
- Sciointermediate
- Apache Beamintermediate
- Flinkintermediate
- BigQueryintermediate
- Snowflakeintermediate
- Redshiftintermediate
- Databricks SQLintermediate
- dbtintermediate
- Airflowintermediate
- Dagsterintermediate
- Prefectintermediate
- Flyteintermediate
- CI/CDintermediate
- observabilityintermediate
- testingintermediate
- monitoringintermediate
- communicationintermediate
- prioritizationintermediate
Required Qualifications
- 3+ years building production quality data solutions with complex domain logic, and use strong SQL to answer questions and debug problems (experience)
- Experience with a cloud data warehouse, for example BigQuery, Snowflake, Redshift, or Databricks SQL (experience)
- Experience with distributed processing frameworks such as Apache Spark or Scio (Scala for Apache Beam), or equivalents like Apache Beam or Flink (experience)
- Comfortable with Python or Scala and with one or more transformation frameworks, for example dbt or equivalent SQL based transformations (experience)
- Used a workflow orchestrator such as Airflow, Dagster, Prefect, or Flyte, and care about reliability, monitoring, and testability (experience)
- Communicate clearly with both technical and non technical partners, and can prioritize and deliver in a changing environment (experience)
Preferred Qualifications
- Experience in platform and developer productivity, experimentation, or ML and AI platform metrics (experience)
Responsibilities
- Build and operate reliable, well-modeled data pipelines and products using SQL, Python, and Scala, with strong testing, observability, and CI/CD. For distributed processing you will use Apache Spark or Scio, or equivalent technologies like Apache Beam or Flink
- Partner in a mixed DE and AE squad to translate complex platform and developer experience use cases into trustworthy datasets, metrics, and ML and AI insights workflows
- Improve data quality, performance, and cost efficiency across existing pipelines, including troubleshooting, backfills, and iterative hardening
- Collaborate with Product, Engineering, and Data Science partners to deliver end to end outcomes, from scoping and modeling to rollout and documentation
- Contribute to agile rituals and take part in a fair support rotation for key pipelines and datasets
Benefits
- general: Flexibility to work where you work best! There will be some in person meetings, but still allows for flexibility to work from home
Tags & Categories