Principal I, Data Engineering (T)

Overview

Principal I, Data Engineering (Science/Tech/Engineering Path) at Alcon, responsible for managing projects to design and implement scalable data systems using advanced technical skills for impactful solutions.

What you'll do

• Design, develop, and operationalize reusable, scalable, and modular data and analytics platforms aligned with Alcon's enterprise architecture.
• Lead the design and implementation of end-to-end cloud-native data solutions on AWS, including data ingestion, transformation, orchestration, and consumption layers.
• Develop and maintain robust, scalable, and high-performance ETL/ELT data pipelines using AWS services such as Glue, Lambda, S3, Step Functions, EventBridge, and related technologies.
• Design and optimize data warehouse solutions using Amazon Redshift, including schema design, performance tuning, workload management, partitioning, and query optimization.
• Partner with data architects to define technical architectures, data integration patterns, and best practices for data and analytics products.
• Design and implement conceptual, logical, and physical data models that support scalable analytics, reporting, and AI/ML use cases.
• Advise, mentor, and coach data engineering and analytics teams on cloud architecture, data modeling, data governance, data quality, and engineering best practices.
• Participate in backlog grooming, sprint planning, technical design reviews, and effort estimation activities.
• Perform data analysis, data profiling, and data modeling to deliver actionable business insights and support strategic decision-making.
• Evaluate, implement, and deploy emerging data engineering, cloud, and analytics technologies to improve platform capabilities, scalability, and team productivity.
• Foster a culture of innovation, collaboration, engineering excellence, automation, and continuous improvement.

What you'll need

• Bachelor's degree in Computer Science, Data Science, Engineering, or a related technical discipline.
• 7+ years of hands-on experience in data engineering, including designing, building, and supporting enterprise-scale ETL/ELT pipelines, data platforms, data lakes, and data warehouse solutions.
• 5+ years of hands-on experience developing cloud-native data solutions on AWS, including S3, Redshift, Glue, Lambda, IAM, CloudWatch, EventBridge, and related services.
• 4+ years of programming experience in Python, Scala, or Java, with strong software engineering fundamentals.
• 3+ years of hands-on experience designing, developing, optimizing, and supporting Amazon Redshift-based data warehouse solutions.
• 3+ years of hands-on experience with dimensional data modeling, star and snowflake schema design, data vault methodologies, and analytical data structures.
• 2+ years of experience designing both batch and streaming architectures for real-time or near real-time data ingestion and processing.
• Strong experience building and maintaining scalable data pipelines using technologies such as AWS Glue, Apache Airflow, Kafka, Spark, or equivalent cloud-native services.
• Experience with two or more database technologies (e.g., PostgreSQL, MySQL, MongoDB) and cloud data warehouse platforms (e.g., Amazon Redshift, Snowflake, BigQuery).
• Experience with source control, CI/CD, infrastructure-as-code, and automated deployment practices for data platforms.
• Proficient with business intelligence and data visualization tools such as Tableau and Amazon QuickSight.
• Strong understanding of data quality, data governance, metadata management, lineage, and monitoring practices.
• Thrives in dynamic, cross-functional team environments and demonstrates a collaborative, team-first mindset.
• Passionate problem solver with strong analytical and troubleshooting skills.
• High learning agility and a continuous improvement mindset.

Nice to have

• Experience in healthcare, digital health, life sciences, or regulated environments.
• Experience with data privacy and security regulations, including HIPAA, GDPR, CCPA, FDA cybersecurity guidance, and security frameworks such as NIST, HITRUST, ISO 27001, and SOC 2.
• Experience with healthcare interoperability standards such as HL7, FHIR, c-CDA, and DICOM.
• Experience with EMR/EHR and PACS integrations.
• Experience with MLOps and AI/ML platforms such as Amazon SageMaker, Vertex AI, or Azure ML.
• Experience building modern data platforms supporting analytics, AI, machine learning, and self-service reporting.
• AWS certifications such as AWS Certified Data Engineer, AWS Solutions Architect, or AWS Analytics Specialty are highly desirable.

Details

• Location: Bangalore, India.
• Work hours: 1 PM to 10 PM IST.
• Relocation assistance: Yes.