Senior/Lead Data Engineer

Job Description
We are seeking an experienced AWS Data Engineer / Data Platform Developer with 3–5 years of experience in data engineering, database development, and cloud-based application development.
The role focuses on designing, developing, and supporting scalable data platforms and cloud solutions using AWS, Snowflake, Amazon Redshift, SQL, and Python, while ensuring strong performance, reliability, security, and compliance.
The ideal candidate should have hands-on experience with:
- Snowflake and Amazon Redshift
- Strong SQL skills, including complex queries, stored procedures, UDFs, database objects, and performance optimization
- Strong Python skills for data processing, scripting, integration, and automation
- ETL/ELT pipeline development and data integration
- Data modelling, data warehousing, and data lake concepts
- Performance tuning, monitoring, troubleshooting, and production support
- Data quality, reliability, and data integrity practices
- Technical documentation and system specification development
Nice to have experience includes:
- Familiarity with AWS services and cloud architecture
- AWS Lambda
- Node.js
- REST API development and integration
- Security concepts, authentication, authorization, and access management
- CI/CD practices
- Cloud-native and microservices-based architectures
- Infrastructure as Code practices
- Relevant AWS certifications such as AWS Certified Data Engineer – Associate or AWS Certified Solutions Architect
The successful candidate should demonstrate strong attention to detail, analytical and troubleshooting skills, effective communication with technical and business stakeholders, the ability to work independently and collaboratively, adaptability, ownership, and a continuous learning mindset.
Job Responsibilities
Design, develop, document, and support scalable data and cloud solutions aligned with architecture standards and business requirements.
Build and maintain ETL/ELT data pipelines and data processing workflows.
Develop and support data solutions using Snowflake, Amazon Redshift, SQL, and Python.
Design and maintain data lakes, data warehouses, databases, and related data platform components.
Develop and optimize SQL queries, stored procedures, UDFs, views, and other database objects.
Develop Python-based data processing, integration, and automation solutions.
Develop and support REST APIs and cloud-based services using AWS technologies in scalable and microservices-oriented architectures.
Participate in system and production deployment planning and support production releases.
Monitor, troubleshoot, and optimize data pipelines, databases, APIs, and cloud services to improve performance, reliability, and scalability.
Identify functionality, performance, or data quality deficiencies and implement improvements to data handling, integrity, conversion, storage, and system behavior.
Implement data quality, validation, monitoring, and governance mechanisms.
Ensure solutions comply with applicable security, regulatory, and company standards, including authentication, authorization, access management, and data protection requirements.
Support compliance with standards and frameworks such as HIPAA, SOC 2, and ISO 27001, where applicable.
Evaluate existing platform capabilities and determine whether standard functionality or custom development is required to address business needs.
Create and maintain detailed technical documentation, system specifications, architecture documentation, and operational procedures.
Collaborate with technical teams, architects, business stakeholders, and external technology vendors.
Support testing, deployment, maintenance, and continuous improvement of platform components.
Department/Project Description
The project focuses on developing and supporting a scalable cloud-based data platform for healthcare and robotics data.
The platform enables efficient data storage, processing, transformation, and analytics using Snowflake, Amazon Redshift, SQL, Python, and AWS services. It is designed to support reliable data workflows, automation, and integration across multiple data sources while maintaining high standards of performance, security, and scalability.
The team is responsible for developing and optimizing data solutions, automating data processing workflows, monitoring platform performance, troubleshooting data-related issues, and supporting reliable production deployments.