Why this role exists:

nThe 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.

nAs 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.

nWhere you will make a difference:

nIn this role you will:

nData Pipeline Engineering & Scalability

n

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

nPlatform Optimisation & Governance

n

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

nTechnical Leadership & Mentorship

n

n
- Provide technical guidance and mentorship to junior engineers, fostering a culture of high-quality code and engineering excellence.
n
- Conduct code reviews and ensure all data engineering work aligns with established architectural standards and security protocols.




n

nContinuous Improvement & Process Excellence

n

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

nWho you will be working with:

n

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

nWhat success looks like:

n

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



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

nHow you will lead:

nIndividual Contributor

n

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

nQualifications and work experience:

nEssential

n

n
- Education: Tertiary qualification in Computer Science, Information Technology, Data Engineering, or a related field.
n
- Experience: Minimum of 5-7 years in Data Engineering or related roles, with extensive experience building production-grade ELT/ETL pipelines.
n
- Technical Mastery: Expert-level proficiency in SQL and deep hands-on experience with Snowflake and dbt (data build tool).
n
- Cloud Proficiency: Demonstrated experience working within AWS or GCP ecosystems to manage large-scale data ingestion and storage.
n
- 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.
n
- Communication: Strong interpersonal skills with the ability to explain complex technical concepts to both technical and non-technical stakeholders.
n

nPreferred

n

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

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