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Senior Data Engineer - Analytics

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💎 Seniority level: Senior, 5 - 8 years

📍 Location: United States

💸 Salary: 167249.0 - 216000.0 USD per year

🔍 Industry: Healthcare

🗣️ Languages: English

⏳ Experience: 5 - 8 years

🪄 Skills: PythonSQLApache AirflowData AnalysisETLGCPAmplitude AnalyticsData engineeringData StructuresCommunication SkillsAnalytical SkillsProblem SolvingData modeling

Requirements:
  • 5 - 8 years of experience in data engineering, analytics engineering, or a related field.
  • Bachelor’s or Master’s degree in Computer Science, Data Science, Information Systems, or a related field, with strong coursework in databases, data structures, and system design, or equivalent industry experience.
  • Strong proficiency in SQL and Python and experience working with cloud data warehouses (e.g., BigQuery), model analytics pipeline and orchestration tools (e.g., dbt, Dagster, Airflow), and self-service analytics tools (e.g., Looker).
  • A solid understanding of analytics database concepts, ELT pipelines, and best practices in data modeling.
  • Ability to work cross-functionally with stakeholders to gather requirements and deliver impactful solutions.
  • Strong problem-solving skills and a passion for building scalable data solutions.
  • [Nice to have] Experience in the healthcare industry or otherwise handling sensitive data.
Responsibilities:
  • Design, develop, and maintain ELT data pipelines to ensure reliable and efficient data processing. Our tools include dbt, Dagster, and GCP.
  • Build and optimize data models to support analytics and reporting needs.
  • Collaborate with analysts and business stakeholders to create and maintain self-service analytics tools that provide meaningful insights. Our primary tools are Looker and Amplitude.
  • Ensure data quality and integrity through testing, validation, and documentation.
  • Monitor and improve analytics database performance, optimizing queries and warehouse costs. Our analytics database lives on BigQuery.
  • Automate and improve our data pipeline workflows for scalability and efficiency.
  • Work closely with product, engineering, and business teams to understand data requirements and translate them into effective solutions.
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