Client Data Engineering Manager

Summary

Join Abacus Insights as the Manager, Client Data Engineering, a pivotal role overseeing a global team of 10-12 professionals. You will lead client implementation efforts, focusing on data engineering processes, particularly ETL, data mapping, and clinical data management. This hands-on position requires extensive expertise in ETL processes and data mapping integration, strong understanding of healthcare systems and clinical data, and proficiency in Python and PySpark. The ideal candidate will also possess advanced SQL skills, familiarity with AWS services, Databricks, and Snowflake, and experience with GitLab or GitHub. You will be responsible for managing day-to-day operations, setting performance metrics, and fostering innovation within the team. This is a growth opportunity within a successful SaaS business.

Requirements

• Proven Experience in Data Engineering: Minimum of 5 years in data engineering with a focus on client implementations
• Strong InterOp Knowledge: Demonstrated expertise in InterOp processes and technology
• Leadership Skills: Experience in managing and mentoring technical teams
• Collaboration with Global Teams: Proven ability to work effectively with offshore and diverse teams
• Efficient Process Management: Track record of streamlining data implementation processes
• Communication Proficiency: Excellent written and verbal communication skills for client and team interactions
• Analytical Abilities: Strong problem-solving skills with a data-driven approach
• Technical Skills ETL Expertise: Extensive experience in Extract, Transform, Load processes, with a proven ability to handle complex data integration tasks
• Data Mapping Proficiency: Skilled in mapping diverse data formats, ensuring seamless data flow and transformation
• Healthcare Knowledge: Strong understanding of healthcare systems and standards, enhancing the ability to address industry-specific data challenges
• Clinical Data Acumen: Experience with clinical data, including familiarity with electronic health records and compliance requirements
• Python/PySpark Skills: Proficient in using Python and PySpark for data manipulation and analysis
• Strong SQL Experience: Advanced SQL skills for querying, optimizing, and managing large datasets efficiently
• AWS Expertise: Proficient in using AWS services for data storage, processing, and analytics
• Databricks Experience: Hands-on experience with Databricks for big data processing and collaborative analytics
• Snowflake Skills: Knowledgeable in using Snowflake for efficient data warehousing and management solutions
• Version Control Experience: Familiarity with code check-in processes and experience using GitLab or GitHub for version control and collaboration

Responsibilities

• Oversee Implementation Team: Lead the team responsible for implementing new clients, ensuring all processes are carried out smoothly and efficiently
• Streamline CMS InterOp Implementation Process: Enhance and optimize the InterOp implementation procedures to improve efficiency and performance
• Collaborate with Offshore Teams: Work closely with offshore teams to align efforts and achieve common goals
• Mentor and Guide New Hires: Provide guidance and support to new team members, fostering a collaborative and productive work environment
• Manage Day-to-Day Operations: Take on some of the current manager’s responsibilities to ensure the seamless execution of daily implementation tasks
• Set Performance Metrics: Establish clear performance objectives and key results for the team, tracking progress and making necessary adjustments
• Foster Innovation: Encourage the team to explore new methods and technologies to continually improve client data engineering processes

Preferred Qualifications

• Advanced Degree: Master's in Data Science, Computer Science, or a related field
• Healthcare Background: Additional experience in healthcare analytics or administration
• Machine Learning: Familiarity with machine learning techniques and tools
• Data Visualization: Proficiency in data visualization tools such as Tableau or Power BI
• DevOps Experience: Understanding of DevOps practices to enhance data engineering processes