Data Engineer

Data engineering at J4RVIS means building the foundations that make enterprise data reliable, accessible, and ready to drive business decisions. You’ll design and own production data pipelines, transform raw data into trusted datasets, and make the technical decisions that keep our data platform scalable, reliable, and cost-efficient.

We’re looking for a Data Engineer with strong hands‑on experience in Snowflake and dbt who understands more than just writing SQL. You’ll build data models, optimise pipelines, enforce data quality, and take ownership of what happens after deployment. You’ll work closely with engineers, architects, and stakeholders across Australia and the Philippines, so technical judgement, proactive communication, and a strong sense of ownership matter.

If you’re ready to build beyond the brief and take real responsibility for the solutions you deliver, this is the role.

About J4RVIS

J4RVIS brings humans and technology together - for businesses built on relationships. We're a consulting business at our core, guiding enterprises through transformation by listening first, then building technology that makes people more capable, not redundant.

Since launching in 2020, we've grown to a team of 110+ people across Australia and the Philippines. We hold Salesforce Summit Partner status alongside partnerships with Anthropic, ElevenLabs, Snowflake, and AWS - spanning business systems, data and integration, conversational AI, and agentic automation. We've been recognised as a Deloitte Fast 50 “Rising Star”, CRN Fast 50, and Great Place to Work in both countries. But the awards describe where we've been, not where we're going.

We go deep in the industries we serve: media, financial services, energy and utilities, because domain expertise is what makes the technology work. We measure success by three business outcomes: customer experience transformation, operational efficiency, and revenue generation. Not hours billed.

AI is central to what we do. We deploy Agentforce at scale, build conversational AI, and develop intelligent automation already running in production. Every team is augmented by AI, and every consultant is developing toward advisory, not just delivery.

We're on a growth spurt to further scale with an APAC expansion underway and a strategy that's locked in. If you want to be part of a firm that's building something that endures, where the culture is real and the ambition is high then this is an exciting time to join.

We're still early in our journey, and that's what makes this an exciting time to join.

Why This Role Exists

Our Data & Integration practice builds the foundations that connect enterprise systems and make data usable. Our clients need more than pipelines that move data - they need trusted, well‑modelled information that supports reporting, analytics, and AI.

This role exists to strengthen that foundation. You’ll use Snowflake as the data platform and dbt as the core transformation and modelling layer , building reliable data pipelines that turn raw data from multiple systems into analytics‑ready datasets.

You’ll own the full lifecycle of your work: from data ingestion and transformation through to testing, deployment, monitoring, and optimisation. This means understanding how your pipelines behave in production, investigating failures, protecting data quality, and making informed decisions about performance and cost.

You’ll work alongside engineers, architects, and stakeholders across Australia and the Philippines, under shared engineering standards and governance practices. The Philippines team is not a support function here. It delivers across every service line we run.

What You’ll Be Doing

• Build and maintain production‑grade data pipelines using Snowflake and dbt, transforming data from multiple sources into trusted, analytics‑ready datasets.
• Develop modular, reusable dbt models using SQL, appropriate materialisations, incremental strategies, and macros.
• Implement dbt tests, source freshness checks, and documentation to maintain data quality, reliability, and transparency.
• Design and maintain ELT workflows, working with ingestion tools such as Fivetran and collaborating with integration teams where custom ingestion is required.
• Design and maintain dimensional data models, including fact and dimension tables, applying appropriate data warehouse architecture patterns.
• Optimise Snowflake query performance, warehouse utilisation, and compute costs.
• Build and maintain orchestration, scheduling, monitoring, and alerting to identify pipeline failures and resolve issues before they affect downstream users.
• Implement CI/CD practices for dbt and data platform changes, including version control, automated testing, and controlled deployments across environments.