Lead Data Engineer

Company Overview

This opportunity is with Weaver Fintech, part of the Weaver Nest group of companies, an ecosystem that includes leading brands like PayJustNow and FinChoice. Weaver Fintech is a purpose-driven, data-centric FinTech company revolutionizing financial services for over 3 million South Africans.

Our mission is to simplify financial journeys, empowering customers to shop, lend, insure, and thrive.

At Weaver Fintech, innovation and inclusion fuel actionable insights, with our Data Engineering & Analytics team at the heart of this transformation.

Role Overview

As a
Senior
Data Engineer, you will lead the evolution of our data platform, architecting and optimizing scalable data pipelines using a modern cloud-based stack.

You will mentor a team, set engineering standards, and drive the migration from legacy Microsoft BI tools to a future-proof, cloud-native architecture.

Key Responsibilities:

• Team Leadership:
  Mentor and guide data engineers, manage workload planning, conduct code reviews, and foster career development.
• Data Architecture:
  Design, build, and maintain robust data pipelines and warehousing solutions on Snowflake (AWS), leveraging best-practice ELT patterns.
• Cloud Integration:
  Architect data ingestion and transformation workflows using Python, AWS S3, AWS Lambda, and DBT.
• Modernization:
  Lead the migration from SSIS/SSAS to cloud-native solutions, ensuring data integrity and minimal business disruption.
• Data Modelling
  : Establish and enforce data modelling standards (e.g., Kimball/star schema), naming conventions, and documentation.
• Performance & Quality:
  Optimize data warehouse performance, implement data quality checks, and maintain observability for all pipelines.
• Security & Compliance
  : Ensure robust data security, privacy, and regulatory compliance across AWS and Snowflake.
• Stakeholder Collaboration:
  Partner with analytics, product, and business teams to deliver high-quality, timely, and secure data solutions.
• CI/CD & Automation
  : Introduce and maintain CI/CD pipelines for data workflows, automate processes, and champion best practices.
• Code Repository Management:
  Use GitHub for version control, code review, and collaborative development of all data engineering assets.

Core Technical skills:

• Python:
  Advanced scripting for ETL/ELT, automation, and integration with AWS and Snowflake
• AWS S3:
  Data lake design, secure storage, lifecycle management, and integration with Snowflake
• AWS Lambda:
  Serverless ETL, event-driven workflows, automation, and error handling
• Snowflake (AWS):
  Data modelling, performance tuning, security, cost optimization, and integration
• Snowflake OpenFlow:
  Workflow orchestration and seamless data movement within AWS ecosystem
• DBT:
  Modular SQL transformations, data quality tests, CI/CD, and analytics engineering
• Microsoft SQL Server:
  Advanced T-SQL, job scheduling, and integration with cloud data platforms
• GitHub:
  Code repository management, version control, code review, CI/CD integration, and secrets management; all using AI

Additional/Legacy Skills:

SSIS & SSAS
: Experience with SQL Server Integration Services (ETL) and Analysis Services (analytical modelling) is valuable.

Note:
These are legacy systems currently in use but will be decommissioned as we transition to our modern cloud stack. Experience in migration and decommissioning is a plus.

Core Soft Skills

• Communication:
  Ability to explain complex technical concepts to both technical and non-technical stakeholders; clear documentation.
• Collaboration:
  Proven experience working in cross-functional teams (product, analytics, business).
• Leadership & Mentorship: Guiding teams, setting standards, and fostering a culture of continuous improvement.
• Problem-Solving:
  Diagnosing and resolving data pipeline and infrastructure issues.
• Adaptability:
  Comfort with evolving technologies and changing business requirements.
• Ownership:
  Taking responsibility for end-to-end delivery and quality.

Required Qualifications

• Must have more than 2 years' experience as a Data Engineering Lead
• Must have more than 5 years' experience in Data Engineering
• Must have more than 7 years' experience with Microsoft SQL Server
• Must have more than 3 years' experience working with AWS S3 and AWS Lambda
• Must have Snowflake AWS and Snowflake OpenFlow working knowledge and practical experience
• Must be well versed, i.e. daily use, in DBT (modular SQL transformation) and GitHub (code repository management)
• Experience: 5+ years in data engineering, with at least 2–3 years in a senior or lead capacity.

Education:

Bachelor's degree in Computer Science, Engineering, Mathematics, or related field (Master's or certifications in AWS, Snowflake, or Microsoft are advantageous).

Domain Knowledge:

Experience in financial services or FinTech is highly valued.

Technical Leadership:

Proven ability to architect large-scale data systems, lead migrations, and mentor teams.

Preferred Qualifications:

• Experience with data orchestration tools (e.g., Airflow, Prefect).
• Familiarity with Infrastructure as Code (e.g., Terraform).
• Advanced certifications in AWS, Snowflake, or Microsoft SQL Server.

Compensation & Benefits

• Competitive salary (market-aligned for FinTech senior data engineers)
• Performance bonuses
• Flexible/remote work options
• Professional development opportunities
• Comprehensive benefits package

Success Metrics (First 6–12 Months)

• Establish a clear data architecture and modernization roadmap.
• Stabilize and optimize pipelines with defined SLAs.
• Implement coding standards and CI/CD pipelines (using GitHub).
• Deliver key data products and reports to improve analytics and decision-making.
• Successfully migrate critical workloads from SSIS/SSAS to the new stack.