Active Jobs at Woolworths

Introduction

• The Engineer Technical Manager: Cloud leads a team focused on designing, developing, and optimizing cloud-based data products that support analytics, reporting, and AI/ML workloads.
• The primary responsibility is to build scalable and high-performance data pipelines, ensuring that structured and unstructured data is ingested, processed, and made available for business insights.
• This role works closely with platform and cloud architecture teams to align data solutions with enterprise cloud strategies, security policies, and governance best practices while providing high-quality, well-structured data for analytics and AI/ML use cases.

Job description

• Develop and optimise cloud-based data products that power analytics, AI/ML, and operational reporting.
• Design and implement scalable, high-performance data pipelines that transform and enrich raw data into structured, reusable datasets.
• Ensure efficient batch and real-time data processing, enabling seamless integration across engineering teams.
• Optimise data pipeline performance to reduce latency and enhance reliability for downstream analytics and AI workloads.
• Develop, implement, and maintain cloud-native data pipelines using AWS services like Glue, Lambda, Step Functions, Kinesis, and EMR.
• Automate data ingestion, transformation, and delivery into data lakes, data warehouses, and analytics platforms.
• Implement change data capture (CDC) and event-driven architectures to support real-time data needs.
• Ensure seamless data integration across structured and unstructured sources (databases, streaming platforms, APIs, etc.).
• Ensure high-quality, structured data availability for AI/ML teams, enabling efficient model training and inference.
• Develop reusable, well-governed datasets for engineering teams, supporting self-service access and automation.
• Work with AI/ML engineers to enable optimised data flows for feature engineering and machine learning pipelines.
• Support advanced data transformations, ensuring data products are aligned with engineering-driven insights and automation.
• Develop CI/CD pipelines for automated testing, deployment, and monitoring of data pipelines.
• Use Infrastructure as Code (IaC) tools like Terraform and CloudFormation to automate provisioning and scaling.
• Implement version control, testing, and rollback strategies to ensure data integrity and reliability.
• Ensure compliance with data governance frameworks, metadata management, and lineage tracking.
• Work closely with platform and cloud architecture teams to align with security and compliance standards.
• Implement automated data quality checks and observability tools to maintain high data accuracy.
• Lead and mentor a team of cloud data engineers, ensuring best practices in cloud-based data product development.
• Collaborate with engineering teams across data, AI/ML, and platform functions to ensure seamless integration.
• Align with platform and cloud architects to leverage cloud infrastructure effectively for data engineering needs.

Minimum requirements

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, or a related field.
• AWS Certifications (AWS Data Analytics, AWS Solutions Architect) are highly advantageous.
• 5 years of experience in data engineering, with at least 2 years focused on cloud-based data product development.
• Experience in building scalable data products for analytics, reporting, and AI/ML workloads.
• Proven expertise in developing and managing cloud-based data pipelines, with a strong focus on automation, scalability, and optimisation.
• Experience working in a cross-functional engineering environment, collaborating with data engineers, AI/ML teams, and platform architects.
• Expertise in building and optimising cloud-based data pipelines using AWS services such as Glue, Lambda, Step Functions, and EMR.
• Strong knowledge of AWS data services, including S3, Redshift, Snowflake, DynamoDB, and Athena.
• Experience working with Kafka, Kinesis, and CDC-based data flows.
• Expertise in Git, Jenkins, Terraform, and CloudFormation for deploying automated data pipelines.
• Proficiency in Python, SQL, and PySpark for data transformation and engineering workflows.
• Ability to monitor, troubleshoot, and optimise data pipelines using AWS CloudWatch.

ADDITIONAL CRITERIA

• Collaboration & Communication: Strong ability to work with engineering, AI/ML, and platform teams.
• Problem-Solving: Ability to troubleshoot and optimise data pipelines for large-scale, cloud-based workloads.
• Continuous Learning: Passion for staying up to date with emerging trends in data engineering and cloud computing.
• Leadership & Mentorship: Proven ability to mentor and grow a team of cloud data engineers.
• Adaptability: Thrives in a fast-moving, evolving technology landscape, adjusting strategies as needed.
• Cultural Fit: Demonstrates integrity, accountability, and a commitment to fostering a collaborative and data-driven organisation.

Apply by: 3 July 2025

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Introduction

• The Engineering Manager: Data Architecture & Governance is responsible for defining and implementing the organisation’s data architecture strategy while ensuring the effective implementation of data governance policies and frameworks defined by the data governance team.
• This role ensures that data is managed, structured, and utilised effectively across the business while maintaining compliance with industry regulations and internal policies.
• The Engineering Manager will oversee the design, integration, and optimisation of data systems, ensuring scalability, security, and accessibility.
• The role involves leading a team of Data Architects, working closely with IT, business units, and security teams to establish and implement data governance practices that enhance data quality, lifecycle management, and decision-making capabilities.
• The Engineering Manager ensures alignment with the organisation’s data governance policies and frameworks, driving consistency and adherence across all data initiatives.

Job description

• Develop and maintain a scalable, secure, and high-performing data architecture that aligns with business and technology strategies.
• Ensure compliance with data governance, security, and privacy regulations by implementing the organisation’s data governance policies.
• Oversee the design and implementation of enterprise data models, frameworks, and policies to support data-driven decision-making.
• Lead and mentor a high-performing team of Data Architects.
• Define and enforce data standards, metadata management, and data lineage practices in alignment with the organisation’s governance policies.
• Collaborate with IT, security, and business teams to implement data governance initiatives and ensure operational and strategic objectives are met.
• Establish and drive data quality and stewardship programs to maintain high data integrity in accordance with governance policies.
• Enable data lifecycle management and retention strategies, ensuring effective data cataloguing and classification.
• Support the integration of on-premise and cloud data solutions to enable seamless interoperability between systems.
• Provide strategic direction on emerging technologies, tools, and methodologies to enhance the organisation’s data capabilities while ensuring governance compliance.

Minimum requirements

• Bachelor’s or Master’s degree in Computer Science, Information Technology, Data Management, or a related field.
• Minimum 6 years of experience in data architecture, data governance, or a related field.
• At least 2 years in a leadership or managerial capacity.
• Strong proficiency in data modeling, database design, and data warehousing.
• Experience with enterprise data governance frameworks (e.g., DAMA DMBOK).
• Knowledge of SQL, NoSQL, and cloud-based data solutions (AWS, Azure, Google Cloud).
• Understanding of ETL processes, metadata management, and data cataloguing.
• Experience with data security, compliance, and regulatory frameworks (GDPR, POPIA, etc.).
• Ability to lead and mentor technical teams while collaborating with business stakeholders.
• Excellent communication skills to translate technical concepts into business language.
• Strong problem-solving and strategic thinking skills to drive long-term data initiatives.

ADDITIONAL CRITERIA

• Strategic Thinking: Ability to align data architecture strategies with organisational goals.
• Collaboration & Communication: Works effectively across business, IT, and governance teams.
• Innovation-Driven: Keeps up with emerging data trends, best practices, and governance methodologies.
• Problem-Solving: Identifies and resolves complex data challenges.
• Adaptability: Thrives in a fast-evolving data landscape, adjusting strategies as needed.
• Cultural Fit: Demonstrates integrity, accountability, and a commitment to fostering a data-driven culture.

Apply by: 7 July 2025