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.

DUTIES AND RESPONSIBILITIES:

· Design, develop, and maintain robust ETL (Extract, Transform, Load) processes to ensure seamless ingestion and transformation of the 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.

· 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.