We are partnering with a public-sector organization undergoing enterprise transformation to appoint a Data Analytics Engineer to join their Innovation Team.

This is a hands-on role for an experienced data professional who enjoys building foundational data infrastructure, enabling analytics and dashboards, and supporting AI solution development in a growing, evolving environment.

The Opportunity

You will play a key role in designing and maintaining data pipelines, building analytics-ready datasets, and translating business needs into actionable insights. The role sits close to both business and technology stakeholders and contributes directly to organization-wide analytics and AI initiatives.

Key Responsibilities

Data Infrastructure & Engineering

• Design, build, and maintain scalable ETL/ELT pipelines for structured and unstructured data

• Integrate data from multiple internal and external sources into data warehouses or data lakes

• Optimise data platforms for performance, scalability, cost efficiency, and AI/analytics use cases

Analytics & Reporting Enablement

• Partner with business teams to understand reporting and analytics requirements

• Build well-structured datasets to support BI tools and AI-driven analytics

• Develop and maintain dashboards and visualisations using Power BI and/or Tableau

Data Governance & Capability Building

• Guide internal stakeholders on data housekeeping, governance standards, and best practices

What Were Looking For

Bachelors degree in Computer Science, Information Systems, Data Engineering, or a related discipline from a reputable university

Minimum 6 years of experience in data engineering, BI development, or data analytics

• Strong technical capability in:

  • SQL and relational databases

  • ETL/ELT frameworks (e.g. dbt, Airflow, Fivetran)

  • Cloud platforms (AWS, GCP, Azure) and data warehouses (Snowflake, BigQuery, Redshift)

  • Python or other scripting languages

  • BI tools such as Power BI and Tableau

• Experience in data modelling, schema design, governance, compliance, and security best practices

• Prior exposure to supporting AI/ML teams through data infrastructure and dataset development