Head of Data Engineering at Lulalend

KEY RESPONSIBILITIES:

Data Engineering Leadership

• Lead and mentor a high performing Data Engineering team, fostering a culture of delivery and continuous improvement.
• Direct line management of Data Engineers, Analytics Engineers & Machine Learning Engineers.
• Drive quality throughout the systems development lifecycle, ensuring that data engineering principles and best practices are being adhered to resulting in scalable solutions that meet security, performance and programming standards.
• Contribute as a key member of the Technology Management team collaborating with all members to ensure that we deliver a world class experience to our customers.

Strategic Planning and Delivery

• Articulation and delivery of a data strategy which considers the factors of people, process and technology aligned to our technology and business strategies.
• Translate business requirements into technical roadmaps, ensuring that our data infrastructure (Snowflake) is leveraged to support key initiatives such as AI-driven growth and internal platform modernization..
• Research and implement new software technologies that benefit our customers and give us a competitive edge.
• Partner with business stakeholders to understand their data needs.
• Collaborate with Product and Engineering to deliver against all agreed roadmaps.
• Oversee the design, development, and maintenance of scalable data pipelines and infrastructure to support the organization's data needs.
• Report on team activities, and ensure accurate planning and time estimation of data engineering work
• Manage budgets and capacity to support delivery.
• Manage key third party suppliers ensuring they are performing and delivering to SLO’s and SLA’s.

Compliance and Regulatory Oversight

• Stay abreast of relevant data privacy laws, regulations, and industry standards, ensuring the organization's data practices remain compliant.
• Develop and maintain policies and procedures for data handling, access controls, and data retention in accordance with regulatory requirements.
• Work closely with legal and compliance teams to address any data privacy or security issues and drive continuous improvement in compliance practices.

Data Governance

• Establish and maintain a framework for data governance, including data quality standards, metadata management, and data classification.
• Define roles and responsibilities for data stewardship and ensure accountability for data management practices throughout the organization.
• Implement tools and processes to monitor and enforce compliance with data governance policies and standards.

THE EXPERIENCE WE’RE LOOKING FOR:

• Degree in a quantitative field (Mathematics, Computer Science, Engineering, Statistics or equivalent). Masters an added bonus
• 8 - 10 Years + data engineering experience, deep understanding of data management principles, data architecture and ELT processes with hands-on experience in building and managing data pipelines utilising continuous integration
• 3-5 Years experience in a leadership role overseeing data engineering, compliance, and governance functions within a technology-driven organization (advantageous)
• In depth experience of building and supporting high performing/scaling cloud based data platforms
• Proven track record of leading successful migrations of key components within the data stack, ensuring minimal disruption and optimal performance.
• Experience in deploying operational embedded data products for business process efficiencies through data activation and ML Ops
• Demonstrated experience collaborating with embedded analysts through data mesh or lakehouse architecture, ensuring analyst empowerment while maintaining standards
• Demonstrable experience working with Azure, Snowflake and DBT (highly advantageous)
• Strong knowledge of data privacy regulations such as POPIA, GDPR, etc., and experience implementing compliance programs to ensure adherence to these regulations.
• Demonstrated expertise in developing and implementing data governance frameworks, including data quality management, metadata management, and data classification.
• Experience working closely with a product team and within an agile environment
• Experience defining and executing on a data strategy (advantageous)
• Financial services and banking experience (advantageous)