Senior Data Engineer

About Lula

Lula is on a mission to simplify business funding for South African SMEs. We build fast, digital-first fintech tools that make cash flow management seamless so business owners can focus on doing what they love. We’re a fast-growing, tech-driven team that values curiosity, collaboration, and high impact over red tape. Speaking of love, we’re looking for Lulas who love to make a difference to join our team and change the game.

Culture Code

• We Embrace Curiosity -We continuously seek better ways to deliver value with a solutions-over-problems mindset.
• We win as One – We collaborate, build strong relationships and value diverse perspectives
• We’re Driven by Purpose -We are passionate and committed to delivering the best products and services for SMEs
• We Execute with Ambition – We set ambitious goals, embrace challenges, and deliver with focus and determination.

ROLE PURPOSE

As a Senior Data Engineer at Lula, you will work at the heart of our data platform, building and running the pipelines and data systems the business relies on. You will work with our data scientists, analytics engineers, machine learning engineers and analysts to deliver reliable data that supports decisions across product, operations, finance, marketing and credit.

Your focus is on owning the design and delivery of complex data pipelines on our Azure and Snowflake platforms, setting the engineering standard in your area, and developing the engineers around you. You will turn ambiguous needs into reliable solutions, mentor more junior colleagues, and help grow small and medium enterprises in South Africa through better use of data.

KEY RESPONSIBILITIES

• Partner with stakeholders to translate business needs into scoped, sequenced delivery plans, and communicate designs, trade-offs and progress clearly to technical and non-technical audiences.
• Own the design and delivery of complex data pipelines and data models end-to-end on our Azure and Snowflake platforms, documenting design decisions and the alternatives considered.
• Create and maintain robust ELT/ETL products for batch, micro-batch and near real-time data pipelines using Airbyte, Event Grid, Azure Data Factory, Kafka or similar tools.
• Assemble data sets that meet functional and non-functional business requirements, and develop and maintain operational and analytical data systems.
• Own the reliability, monitoring, data quality and cost of the pipelines in your area, and drive fixes and improvements when they degrade.
• Follow test-driven development practices, and set and uphold standards for testing, code quality and documentation through code review.
• Work as part of a multi-disciplinary data team (Data, Analytics, Machine Learning Engineers) and collaborate with analysts and data scientists across the business.
• Mentor junior and intermediate colleagues, and contribute to hiring and onboarding.
• Demo work to technical and non-technical stakeholders, and create documentation and training material for the solutions delivered.

THE SKILLS AND EXPERIENCE WE’RE LOOKING FOR

• A degree in computer science, engineering, information systems, mathematics or a related quantitative field, or equivalent practical experience
• 3-5 years’ experience in data engineering
• Strong hands-on experience with Snowflake, including warehouse and resource management, semi-structured data handling (VARIANT, JSON), and performance and cost optimisation
• Experience with dbt (Core or Cloud): models, tests, macros, snapshots, and documentation generation
• Experience using schemachange, Flyway, Liquibase, or a similar tool to manage database change control as code
• Proficiency in Jinja templating for building dynamic, reusable SQL/config
• Advanced working knowledge of SQL (DDL, DML, JSON) and solid experience managing incremental/batch loading methodologies (CDC, CT, CDC-style watermarking)
• Proven experience building ingestion pipelines from diverse source types: APIs, flat files, relational/NoSQL databases, and Azure Table Storage
• Skilled and experienced in the Azure (or AWS/GCP) cloud platform
• Good understanding of relational data structures, including keys, constraints, and triggers
• Experience with performance tuning and optimisation of RDBMS and/or cloud data warehouses
• Experience with relational and NoSQL database technologies (MS SQL Server, MongoDB, CosmosDB, etc.)
• Ability to design and implement logical and physical data models that support organisational needs
• Good understanding of data modelling, data management and governance methodologies
• Strong analytic skills working with structured, semi-structured and unstructured data sets
• Proficiency in Python (preferred), Java, or Scala
• Practical experience applying generative AI / LLM tools to engineering or data workflows (e.g. Copilot, ChatGPT, Claude, or similar) and agentic development
• Experience implementing CI/CD pipelines through technologies such as GitLab, GitHub Actions, Azure DevOps, etc.
• Experience deploying data systems in an Infrastructure-as-Code (IaC) manner, preferably using Terraform
• Ability to produce high-quality technical documentation as a routine part of delivery
• Experience supporting and working with cross-functional teams in a dynamic environment
• Communicates effectively with both technical and non-technical stakeholders
• Experience mentoring or technically guiding other engineers

Advantageous

• Experience with Airflow and Airbyte
• Experience with event-driven systems such as Kafka or Azure Event Grid
• Experience building feature pipelines or data products that support machine learning and analytics workloads
• An interest in the FinTech space

OUR TECH STACK

• Azure (Functions, databases, blob storage)
• DBT
• Snowflake
• Airflow
• Airbyte
• Event Grid