Data Engineering Lead

Data Engineering Lead Description We are seeking a talented Data Engineering Lead to design, develop, and govern our data architecture and pipelines. Join as soon as possible. The candidate will need to be collaborative, organised, think out-of-the-box, and be ready to pursue new opportunities. Most importantly, this role is for an individual who is passionate about making a difference through healthcare. We have good budget for this position. Key Responsibilities Lead, mentor, and manage a team of data engineers, fostering a culture of technical excellence and continuous improvement. Act as a hands-on technical expert, designing and implementing scalable and robust data architectures for data warehousing and enterprise data platforms. Communicate effectively with stakeholders, translating complex business needs into technical requirements and solutions. Define and enforce data engineering best practices, including coding standards, documentation, and quality assurance. Develop and optimize data pipelines and ETL/ELT/CDC (Change Data Capture) workflows using tools such as Fivetran and Cloud Composer. Collaborate with data scientists, product managers, and business stakeholders to define data requirements and create logical and physical data models. Manage and administer various database systems, including BigQuery, SAP HANA, and PostgreSQL. Ensure data quality, integrity, and security across all data platforms and pipelines. Work with our AI/ML teams to design data serving layers and feature stores that support Vertex AI workloads. Design and develop reporting frameworks and data marts to support business intelligence needs. Define and implement data governance, master data management, and data cataloging strategies. Contribute to the full data lifecycle: requirements gathering, architecture, data modeling, development, testing, and deployment. Troubleshoot and resolve data platform issues to ensure high availability and optimal performance. Document technical designs, data lineage, and architecture for cross-functional reference. Required Qualifications Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Science, or a related field. Proven experience in a leadership or senior role, mentoring and guiding junior engineers. Exceptional communication and interpersonal skills, with the ability to articulate complex technical concepts to diverse audiences. Proficiency in one or more backend languages/frameworks, with a strong preference for Python or Go. Experience with building RESTful APIs and designing microservices for data delivery. Solid grasp of data modeling fundamentals, including Kimball and Inmon methodologies. Proficiency in writing complex SQL queries and experience with SQL and NoSQL databases. Familiarity with data warehousing concepts and best practices, including CDC. Strong version-control habits (Git) and experience with CI/CD pipelines. Excellent problem-solving and collaboration skills. Passion for continuous learning and adapting to emerging data technologies. Preferred Qualifications Hands-on experience designing and deploying production-grade data warehouses. Deep experience with Google Cloud Platform (GCP) BigQuery for large-scale analytical workloads. Cloud Composer for orchestrating complex data pipelines. Vertex AI for AI/ML model serving and feature stores. Experience with other cloud providers (AWS, Azure) and their data services. Working knowledge of data governance frameworks, master data management, and data cataloging tools. Experience with data ingestion tools like Fivetran. Business-intelligence expertise in building dashboards and reports with Power BI or Tableau. Familiarity with other data technologies such as SAP HANA. Understanding of MLOps concepts and their application to data pipelines. Contributions to open-source data projects or technical blogging/presentations. Application Process: Please send your resume to [careers@chromeis.com] with a relevant subject line. If your resume is shortlisted, you will be invited to take an online AI-based assessment. Candidates who pass this test will move on to the next stage: an on-call interview with the end client. Successful candidates from all rounds will receive an offer based on the initial discussion during the first call.