Data Engineer

Company Overview

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.

We foster innovation, inclusion, and 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.