AWS Data Engineer

Candidate will be responsible for creating and managing the technological part of data infrastructure in every step of data flow.

From configuring data sources to integrating analytical tools — all these systems would be architected, built, and managed by a general-role data engineer.

Minimum applicable experience (years): 5+ years working experience

Minimum qualifications: Bachelor's degree in Computer Science or Engineering (or similar) Honors degree in Computer Science or Engineering (or similar) AWS Certified Solutions Architect or AWS Certified Data Analyst

Required nature of experience: Experience with AWS services used for data warehousing, computing and transformations, i.e., AWS Glue (crawlers, jobs, triggers, and catalog), AWS S3, AWS Lambda, AWS Step Functions, AWS Athena, and AWS CloudWatch.

Experience with SQL and NoSQL databases (e.g., PostgreSQL, MySQL, DynamoDB) Experience with SQL for querying and transformation of data.

Skills and Knowledge (essential):

1. Strong skills in Python (especially PySpark for AWS Glue)
2. Strong knowledge of data modeling, schema design and database optimization
3. Proficiency with AWS and infrastructure as code

Skills and Knowledge (desirable):

1. Knowledge of SQL, Python, AWS serverless microservices
2. Deploying and managing ML models in production
3. Version control (Git), unit testing and agile methodologies

Data Architecture and Management (20%):

1. Design and maintain scalable data architectures using AWS services for example, but not limited to, AWS S3, AWS Glue and AWS Athena.
2. Implement data partitioning and cataloging strategies to enhance data organization and accessibility.
3. Work with schema evolution and versioning to ensure data consistency.
4. Develop and manage metadata repositories and data dictionaries.
5. Assist and support with defining, setup and maintenance of data access roles and privileges.

Pipeline Development and ETL (30%):

1. Design, develop and optimize scalable ETL pipelines using batch and real-time processing frameworks (using AWS Glue and PySpark).
2. Implement data extraction, transformation and loading processes from various structured and unstructured sources.
3. Optimize ETL jobs for performance, cost efficiency and scalability.
4. Develop and integrate APIs to ingest and export data between various source and target systems, ensuring seamless ETL workflows.
5. Enable scalable deployment of ML models by integrating data pipelines with ML workflows.

Automation, Monitoring and Optimization (30%):

1. Automate data workflows and ensure they are fault tolerant and optimized.
2. Implement logging, monitoring and alerting for data pipelines.
3. Optimize ETL job performance by tuning configurations and analyzing resource usage.
4. Optimize data storage solutions for performance, cost and scalability.
5. Ensure the optimization of AWS resources for scalability for data ingestion and outputs.
6. Deploy machine learning models into production using cloud-based services like AWS Sagemaker.

Security, Compliance and Best Practices (10%):

1. Ensure API security, authentication and access control best practices.
2. Implement data encryption, access control and compliance with GDPR, HIPAA, SOC2 etc.
3. Establish data governance policies, including access control and security best practices.

Development Team Mentorship and Collaboration (5%):

1. Work closely with data scientists, analysts and business teams to understand data needs.
2. Collaborate with backend teams to integrate data pipelines into CI/CD.
3. Assist with developmental leadership to the team through coaching, code reviews and mentorship.
4. Ensure technological alignment with B2C division strategy supporting overarching hearX strategy and vision.
5. Identify and encourage areas for growth and improvement within the team.

QMS and Compliance (5%):

1. Document data processes, transformations and architectural decisions.
2. Maintain high standards of software quality within the team by adhering to good processes, practices and habits, including compliance to QMS system, and data and system security requirements.
3. Ensure compliance to the established processes and standards for the development lifecycle, including but not limited to data archival.
4. Drive compliance to the hearX Quality Management System in line with the Quality Objectives, Quality Manual, and all processes related to the design, development and implementation of software related to medical devices.
5. Safeguard confidential information and data.

This job description is not a definitive or exhaustive list of responsibilities and is subject to change depending on changing business requirements.

Employees will be consulted on any changes.

Employee's performance will be reviewed based on the agreed-upon objectives.

Seniority level: Mid-Senior level

Employment type: Full-time

Job function: Information Technology

Industries: IT System Data Services, Medical Equipment Manufacturing, and Technology, Information and Media