Why this role exists:

The Senior Engineer (Data) is responsible for designing, building, and maintaining robust and scalable data pipelines to support Officeworks enterprise data strategy and the evolution of AI-driven capabilities. By leveraging the Snowflake enterprise data platform and modern ELT frameworks, the role ensures that high-quality data from core systems like SAP, Salesforce, and Adobe is accessible for advanced analytics and self-serve reporting.

As a senior member of the Technology team, this role plays a critical part in driving data democratisation and continuous improvement, providing the technical foundations that enable Officeworks to make data-led decisions and improve customer outcomes.

Where you will make a difference:

In this role you will:

Data Pipeline Engineering & Scalability

- Design and build scalable, automated data pipelines using Snowflake, AWS/GCP, and modern ELT frameworks.
- Lead the ingestion of diverse datasets from key enterprise systems including SAP, Salesforce, and Adobe into the Enterprise Data Platform (EDP).
- Implement complex transformation logic using dbt and SQL pipelines to ensure data is business ready.

Platform Optimisation & Governance

- Monitor and optimise data pipeline performance and cloud compute cost efficiency within the Snowflake environment.
- Develop and maintain technical documentation and metadata to ensure data lineage and transparency.
- Support the transition toward a self-serve analytics model by ensuring data architectures support democratised access.

Technical Leadership & Mentorship

- Provide technical guidance and mentorship to junior engineers, fostering a culture of high-quality code and engineering excellence.




- Conduct code reviews and ensure all data engineering work aligns with established architectural standards and security protocols.

Continuous Improvement & Process Excellence

- Identify opportunities to automate manual data processes and improve the reliability of the data ecosystem.
- Drive continuous improvement initiatives in data engineering workflows to reduce technical debt and increase delivery velocity.
- Champion best practices in CI/CD and DevOps for data engineering within the Data & AI hub.

Who you will be working with:

- Technology Teams: Collaborate with the Data Architect, Data Modellers, and the AI team to align engineering efforts with broader architectural and AI roadmaps.
- Analytics Team: Partner with Data Scientists and Analysts to understand data requirements and ensure the delivery of high-quality datasets.
- Delivery Hub: Contribute to a high-performance and collaborative engineering culture.
- Stakeholders: Engage with broader Technology leaders to support the execution of the enterprise data strategy and the migration from legacy systems.

What success looks like:

- Reliable Data Assets: Successful deployment and maintenance of stable, scalable data pipelines with minimal downtime and high data integrity.
- Technical Excellence: Delivery of high-quality, well-documented code that adheres to Officeworks engineering standards and promotes reusability.
- Operational Efficiency: Measurable improvements in pipeline performance and cost-optimisation within the cloud data environment.
- Continuous Growth:



Demonstrable contribution to the capability uplift of the engineering team through effective mentorship and the implementation of process improvements.

How you will lead:

Individual Contributor

- Lives our Officeworks values and behaviours
- Proactively contributes to a safe working environment, escalates appropriately if there are unsafe conditions or inappropriate behaviour
- Operates in line with applicable Officeworks company policies and Code of Conduct
- Demonstrates a robust sense of personal accountability and curiosity to learn and develop

Qualifications and work experience:

Essential

- Education: Tertiary qualification in Computer Science, Information Technology, Data Engineering, or a related field.
- Experience: Minimum of 5-7 years in Data Engineering or related roles, with extensive experience building production-grade ELT/ETL pipelines.
- Technical Mastery: Expert-level proficiency in SQL and deep hands-on experience with Snowflake and dbt (data build tool).
- Cloud Proficiency: Demonstrated experience working within AWS or GCP ecosystems to manage large-scale data ingestion and storage.
- Adaptability: Proven ability to adapt to new technologies (including shifting from traditional ML to AI frameworks) and a strong cultural fit for a fast-paced, evolving environment.
- Communication: Strong interpersonal skills with the ability to explain complex technical concepts to both technical and non-technical stakeholders.

Preferred

- Industry Context: Experience within a large-scale Retail or Omnichannel environment, particularly involving SAP or Salesforce integrations.
- Advanced Tooling: Familiarity with GitHub for version control and automated CI/CD deployment workflows.

📌 Senior Engineer - Data (Bengaluru)
🏢 Officeworks
📍 Bengaluru