Data Analyst Engineer

Role Overview

The Data Analyst Engineer builds and maintains the data pipelines that power PurpleLab’s data-driven insights and healthcare analytics. You’ll combine engineering and analytical skills to deliver high-quality healthcare data for downstream reporting and decision-making.

Responsibilities

• Design, develop, and maintain ETL processes for seamless ingestion and transformation into a Data Warehouse.
• Use Airflow (or similar) to orchestrate pipelines with custom operators, sensors, and hooks.
• Implement data quality checks and validation using Spark and SQL.
• Analyze data for trends and patterns to proactively identify and fix data quality issues.
• Partner with data science, business intelligence, and other teams to ensure data accuracy, consistency, and security.
• Help design and implement data assimilation and reporting strategies.
• Champion data governance best practices and stay current on emerging data technologies.
• Build data integration and consolidation techniques to ensure reliable data transfers.
• Document and perform ad-hoc analyses to support business needs and proposed solutions.

Requirements

• Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or related field (Master’s preferred).
• 3+ years in data engineering / ETL development (preferably healthcare or life sciences).
• Proficiency in SQL, Python, and Spark.
• Experience in Agile environments, specifically Scrum.
• Experience with workflow orchestration tools such as Apache Airflow.
• Strong understanding of ETL, data pipelines, and cloud-based data solutions (AWS, GCP, or Azure).
• Familiarity with data modeling, database design, and data warehousing concepts.
• Understanding of data quality, governance, and data security.
• Strong communication and documentation skills for technical and non-technical stakeholders.

Nice to Have

• Experience with big data tools/warehouses such as Hadoop, Snowflake, or Databricks.