Datawarehouse Engineer

Introduction

Company Overview

Hello Group is a leading fintech committed to delivering innovative products that create impact and drive customer success. We thrive in fast-paced, collaborative environments where ideas turn into solutions that shape the future

Why Join Us?

At  Hello Group , you’ll be part of a forward-thinking, innovative team where quality, collaboration, and personal development are key. You’ll have the opportunity to learn from skilled peers, work on cutting-edge products, and advance your career in a supportive environment.

About the Role

We’re looking for a passionate a Data Warehouse Engineer (SQL Server + Python) that will build and automates data warehouse solutions using SQL Server and Python, with exposure to Microsoft Fabric as an emerging analytics platform. This role will improve reliability and automation of DW delivery through scripted ETL/ELT, data-quality checks, and audit logging, and supports gradual Fabric adoption by integrating Python workflows where applicable.  This is a full-on-site vacancy situated in Centurion.

What  Hello Group  Offers

• Onsite Barista – Because life’s too short for bad coffee!

• Exciting Team Events – Work hard, play harder!

• Teambuilding Activities – Get to know your teammates beyond the screen!

• A Culture That Feels Like Family – No corporate robots here—just real people doing great things!

• A Top-Notch Office Space – Where inspiration meets innovation

Please note: If you have heard back from us for vetting purposes, please take your application as unsuccessful, and that you have not met the required criteria.

Duties & Responsibilities

1. SQL Server DW Development

Develop and maintain SQL Server DW structures (tables, views, stored procedures) in line with team standards (Sandbox → Extract → Transform). Support DWIntake-driven delivery including source onboarding, target naming, schedules, and production handover.

2. Python ETL/ELT Automation

Automate ETL/ELT processes using Python (pandas, pyodbc, SQLAlchemy). Build reusable scripts for extracts, transforms, loads, file/API ingestion, and scheduled jobs with robust connectivity, failure handling, and recoverable run design.

3. Data Quality & Audit Logging

Implement data-quality checks in Python scripts and pipelines to prevent bad data landing in the DW. Design audit logging so runs are recoverable, traceable, and reviewable. Support Table Compare and monitoring workflows for assigned datasets.

4. Fabric Enablement

Contribute to Fabric adoption by integrating Python workflows where applicable (notebooks, orchestration helpers, validation scripts). Collaborate with Fabric-focused colleagues on parity, testing, and handoff between SQL Server and Fabric artefacts.

5. Operational Support & Incidents

Respond to operational incidents for owned scripts and jobs. Escalate Finance vs DW ownership correctly. Maintain high reliability of owned scheduled processes.

6. Source Control & Collaboration

Use Git for all owned code with clear commit discipline and rollback paths. Collaborate with DW and BI teams; assist with onboarding on Python automation patterns used by the team.

7. Governance & Documentation

Apply DW governance practices: naming, documentation, and clear ownership of automated processes. Ensure production changes are traceable to intake/Jira references.

8. Stakeholder Delivery

Deliver assigned intake and automation work within agreed timelines. Communicate delays proactively and support BI/data availability queries timeously.

Desired Experience & Qualification

Education

National Diploma / Bachelor’s degree in Information Technology, Computer Science, Data Engineering, or equivalent — OR equivalent demonstrable DW / data-engineering experience. Python / data-engineering certifications preferred. Microsoft Fabric or Azure Data training advantageous.

Experience

3–5 years SQL Server experience (T-SQL, stored procedures, indexing, performance tuning). Minimum 1 year practical Python for data engineering (ETL/ELT automation, APIs; pandas, pyodbc / SQLAlchemy). Experience implementing data-quality checks and audit logging in scripts/pipelines. FinTech / high-volume transactional experience highly desirable.

Technical Knowledge

Strong SQL Server (SSMS / T-SQL). Proficiency in Python for data engineering. Git and scripting practices that support auditability and rollback. Solid DW concepts and governance. Preferred: Microsoft Fabric (pipelines, lakehouse, warehouse), cloud data integration tools, Power BI handoff.

Analytical Skills

Ability to diagnose script and SQL failures methodically, distinguish root cause from symptoms, and design checks that prevent bad data rather than fixing after the fact.

Communication

Clear verbal and written communication with DW, BI, and Finance stakeholders. Able to explain technical issues and timelines to non-specialists.

Work Style

Organised, audit-minded, and delivery-focused. Comfortable working in a small team with clear ownership. Able to prioritise under operational time pressure.

Systems Exposure

SQL Server; Python (pandas, pyodbc, SQLAlchemy); Git; ETL/ELT and data-quality frameworks; APIs; Microsoft Fabric (preferred); Hello Group DW tooling (DWIntake, Procedure Monitor, Table Compare) as applicable.

Interested?

Please apply on the PNET site directly.

If you do not receive a response within a month of the closing date, please accept that your application was not successful on this occasion. Regret correspondence will only be sent to interviewed candidates.

Your CV will be kept on our Database.