Consensys
Senior Data Engineer
United States, Canada, LATAM, EMEA (Remote)Full-timeGlobal
š° USD 156,000 - 187,000/yr
š Midš Remote
RemoteRemote work position availableActivePosted within the last 30 days
Job Description
[AI-summarized by JobStash]
You will design, build, and maintain robust data pipelines that integrate sources across the business. You will collaborate with analysts, stakeholders, and engineering teams to gather requirements and deliver reliable data solutions. You will document pipelines and processes, develop and optimize data models, ensure data quality and governance, orchestrate and monitor pipeline execution, deploy and manage infrastructure as code, and automate CI/CD and reporting workflows to enable scalable analytics.
Requirements
- āOver 6 years of experience as a Data Engineer
- āExperience using Trusted Execution Environments (TEEs) to securely process sensitive user data
- āStrong SQL skills
- āExperience with cloud data warehouses such as Snowflake BigQuery or Redshift
- āHands-on experience with transformation and orchestration tools such as dbt Airflow or Dagster
- āProficiency with Python or other scripting languages for ETL and automation
- āFamiliarity with data governance and metadata management such as DataHub
- āExperience deploying and managing infrastructure as code such as Terraform or Pulumi
- āExposure to data integration and ingestion tools such as Airbyte or Segment
- āExperience with big data and distributed processing such as Apache Spark AWS EMR and S3
- āExperience maintaining and improving reporting solutions and dashboards such as Preset Superset or Cube.dev
- āFamiliarity with CI/CD practices and automation such as GitHub Actions
- āWillingness to submit to background checks including employment education and criminal record checks
Responsibilities
- āDesign data pipelines
- āBuild data pipelines
- āMaintain robust data pipelines
- āIntegrate data sources across the business
- āCollaborate with analysts and business stakeholders
- āAlign timelines and discuss architecture with engineering teams
- āDocument data pipelines and best practices
- āDevelop and optimize data models
- āEnsure data quality security and governance
- āOrchestrate and monitor pipeline execution
- āDeploy and manage infrastructure as code
- āBuild and tune big data pipelines using SQL Python and distributed processing frameworks
- āWork with cloud data warehouses to enable insights and analytics
- āMaintain and update reporting solutions and user dashboards
- āAutomate workflows and improve CI/CD pipelines
Benefits & Perks
- āCompetitive benefits
- āEquity
- āUnlimited vacation/holidays
- āFlexible working arrangements
- āRemote first
Tech Stack
AirbyteCubedata modelCube.devtrusted execution environmentSnowflakeAirflowApache Sparkdata pipelineBigQuery