Cloud Data Engineer
Woolpert
AzureCloudJavaScriptPythonSQLTableau
Data Engineer – Foundations
Mighty Networks
full-time
Posted on:
Location Type: Remote
Location: Remote • 🇺🇸 United States
Visit company websiteSalary
💰 $60,000 - $100,000 per year
Job Level
Mid-LevelSenior
Tech Stack
AirflowAWSCloudPostgresPythonRubyRuby on RailsSQL
About the role
- Design, build, and maintain data pipelines that integrate multiple systems (application, marketing, CRM, finance) into a central data warehouse.
- Develop and manage data models and transformations using dbt, ensuring clear lineage, testing, and documentation.
- Implement and monitor data orchestration workflows (dbt, Airflow, or AWS tools like Kinesis) for reliability and freshness.
- Partner with application engineers (e.g., in a Ruby on Rails environment) to define and capture analytics events and improve data collection.
- Maintain data quality, consistency, and validation — identifying anomalies and ensuring trustworthy outputs.
- Use SQL (Postgres, Snowflake) and Python for analysis, debugging, and automation.
- Collaborate with third-party partners such as Sundial and data vendors to integrate external data sources effectively.
- Drive adoption of self-service analytics, enabling product, marketing, and finance teams to independently explore and use data.
- Champion data best practices — from documentation and transparency to governance and reproducibility.
- Leverage AI-assisted tools for data discovery, documentation, and code generation to accelerate development and improve data quality.
- Experiment with AI-powered analytics and automation to surface insights faster and enhance self-service data experiences across teams.
Requirements
- 5+ years of experience as a Data Engineer or similar role in analytics or data infrastructure.
- Proven expertise with SQL (especially Postgres and Snowflake) for data modeling, validation, and analysis.
- Experience with dbt and modern data orchestration tools (Airflow, AWS Kinesis, etc.).
- Strong understanding of data modeling principles — fact/dimension design, normalization vs. denormalization, and schema optimization.
- Proficiency in Python for data manipulation, testing, and pipeline automation.
- Hands-on experience with AWS data tools and cloud-native data ecosystems.
- Demonstrated ability to partner cross-functionally with engineering, analytics, finance, and marketing teams.
- Excellent communication and documentation skills — able to make complex data systems understandable and accessible.
- A rigorous, skeptical approach to data analysis: you validate, cross-check, and verify before drawing conclusions.
- Curiosity about emerging AI technologies and how they can optimize data engineering workflows, analytics, and business intelligence.
- Comfort using AI coding assistants or LLM-powered tools (e.g., for SQL generation, dbt documentation, or debugging) as part of your development process.
- Passion for building self-service data solutions that empower others to be data-informed.
- Bonus: Experience integrating systems like HubSpot, Stripe, or marketing automation platforms.
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
SQLPostgresSnowflakePythondbtAirflowAWS Kinesisdata modelingdata validationdata pipeline automation
Soft skills
communication skillsdocumentation skillscross-functional collaborationdata analysiscuriosityproblem-solvingattention to detaildata governanceself-service data solutionscritical thinking
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