AWS Data Engineer

Job Description

AWS Data Engineer

INTRODUCTION :

An exciting opportunity has become available within the medical technology and devices industry for an AWS Data Engineer. Our client is an impact-driven digital health company passionate about affordable access to healthy hearing for everyone everywhere. This is an opportunity to be part of making a change and impacting human lives.

The position is based in Pretoria East.

PURPOSE :

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 REQUIREMENTS :

Minimum education (essential) :
Bachelors degree in Computer Science or Engineering (or similar)

Minimum education (desirable) :
Honors degree in Computer Science or Engineering (or similar)
AWS Certified Data Engineer or
AWS Certified Solutions Architect or
AWS Certified Data Analyst

Minimum applicable experience (years) :
5 years working experience

Required nature of experience

1. Data Engineering development
2. Experience with AWS services used for data warehousing, computing, and transformations (i.e., AWS Glue, AWS S3, AWS Lambda, AWS Step Functions, AWS Athena, and AWS CloudWatch)
3. Experience with SQL and NoSQL databases (e.g., PostgreSQL, MySQL, DynamoDB)
4. 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
4. English vernacular due to international interaction
5. South African Citizens only

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

RESPONSIBILITIES :

Data Architecture and Management (20%)

1. Design and maintain scalable data architectures using AWS services (e.g., AWS S3, AWS Glue, 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. Comply to ISO, CE, FDA (and other) standards and requirements as is applicable to assigned products.
6. 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. If you do not hear from us within 30 days, please consider your application unsuccessful.

Key Skills: Apache Hive, S3, Hadoop, Redshift, Spark, AWS, Apache Pig, NoSQL, Big Data, Data Warehouse, Kafka, Scala

Employment Type : Full-Time

Experience : years

Vacancy : 1