Data Analyst Engineer

About the role

The Data Analyst Engineer will play a critical role in building and maintaining the data pipelines that fuel our data-driven insights and empower our healthcare offerings by leveraging both technical expertise and analytical skills to ensure the highest quality healthcare data is available for analysis.

Responsibilities

• Design, develop, and maintain robust ETL (Extract, Transform, Load) processes to ensure seamless ingestion and transformation of data to be housed in the Data Warehouse.
• Utilize Airflow (or similar workflow orchestration tool) to manage data pipelines, including custom operators, sensors, and hooks.
• Implement robust data quality checks and validation processes using tools like Spark and SQL.
• Analyze data for trends and patterns to proactively identify and resolve data quality issues.
• Partner with cross-functional teams (data science, business intelligence, etc.) to ensure data accuracy, consistency, and security.
• Support the design, development, testing, and implementation of cost-effective data assimilation and reporting strategies.
• Champion data governance best practices and stay up-to-date on emerging data technologies and trends.
• Develop and implement data integration and consolidation techniques to ensure high-quality data transfers.
• Document and conduct ad-hoc analyses to support business needs, sales efforts, and proposed solutions.
• Collaborate on projects as needed from senior leadership.
• Perform other duties as assigned to support business needs and company objectives.

Requirements

• Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or a related field required (Master’s degree preferred).
• 3+ years of experience in data engineering, ETL development, or a related role, preferably in healthcare or life sciences.
• Proficiency in SQL, Python, and Spark for data transformation and analysis.
• Experience working in an Agile environment, specifically Scrum.
• Experience working with workflow orchestration tools such as Apache Airflow.
• Strong knowledge of ETL processes, data pipelines, and cloud-based data solutions (AWS, GCP, or Azure).
• Familiarity with data modeling, database design, and data warehousing concepts.
• Experience with big data technologies (e.g., Hadoop, Snowflake, Databricks) is a plus.
• Understanding of data quality frameworks, governance best practices, and data security principles.
• Strong problem-solving skills with the ability to analyze complex data sets and identify trends.
• Ability to collaborate with cross-functional teams, including data science, business intelligence, and leadership.
• Excellent communication and documentation skills to support technical and non-technical stakeholders.