Data Engineer (Snowflake & dbt)

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.

• Apply data governance and security practices, including access controls and appropriate handling of sensitive data.

• Troubleshoot production incidents, investigate data discrepancies, and implement lasting fixes rather than relying on manual workarounds.

• Collaborate with analysts, integration developers, architects, and stakeholders to translate business requirements into reliable data solutions.

• Contribute to technical designs, code reviews, estimates, documentation, and runbooks, helping maintain consistent engineering standards.

What We're Looking For

We care much more about depth in the essentials below than about a long list of platforms.

Essential

• Snowflake: Hands-on experience building and supporting production data pipelines and working with Snowflake as a cloud data warehouse.

• dbt: Practical experience delivering production dbt projects, including model development, materialisations, incremental models, testing, and documentation.

• SQL: Strong working command of SQL, including complex transformations, query optimisation, and troubleshooting.

• Data engineering: Experience building and maintaining production ETL/ELT pipelines, with a solid understanding of ingestion, transformation, and downstream consumption.

• Data modelling: Understanding of dimensional modelling, fact and dimension tables, and the design of reusable, analytics-ready datasets.

• Python: Working knowledge of Python for automation, data processing, or pipeline support.

• Production ownership: Experience monitoring pipelines, investigating failures, improving reliability, and maintaining data quality.

• Engineering practices: Experience with Git, code reviews, testing, and structured development and deployment workflows.

Strongly preferred

• Snowflake performance tuning, warehouse sizing, workload management, and cost optimisation.

• Advanced dbt capabilities, including macros, snapshots, incremental strategies, source freshness, and reusable modelling patterns.

• CI/CD for dbt using GitHub Actions, GitLab CI/CD, Azure DevOps, or equivalent tools.

• Data orchestration using Airflow, Dagster, Prefect, or similar platforms.

• Data ingestion and replication using tools such as Fivetran.

• Data quality monitoring, observability, alerting, and production incident management.

• Experience with data security, role-based access control, masking, and governance practices.

• Experience integrating enterprise source systems, such as CRM, finance, and operational platforms.

Nice-to-have

• Experience with Data Vault, medallion architecture, or other established data architecture patterns.

• Exposure to AWS or Azure data services.

• Experience supporting Power BI or other business intelligence platforms.

• Familiarity with preparing reliable datasets for AI and machine learning use cases.

• Experience in consulting, professional services, or client-facing delivery environments.

• Relevant Snowflake or dbt certifications.

How You Work Matters

Technical skills matter, but so does how you approach the work.

• Ownership: You follow through, take responsibility, and care about the reliability of what you build.

• Proactivity: You identify risks, challenge inefficient processes, and suggest improvements without waiting to be asked.

• Communication: You explain technical decisions clearly, raise issues early, and work effectively with technical and non-technical stakeholders.

• Judgement: You investigate independently, make practical decisions, and know when to bring others in.

• Quality: You write maintainable code, test your work, document what matters, and build for production rather than just the happy path.

• Collaboration: You share knowledge, welcome feedback, and help raise the engineering standard across the team.

Why Join J4RVIS

Do meaningful work: Build data foundations that support business transformation, analytics, and AI.

Accelerate your growth: Work alongside experienced engineers, architects, and ecosystem leaders across data, integration, and AI.

Own your impact: Make technical decisions and take responsibility for the solutions you deliver.

Be part of something early: Join a Summit Partner in a high-growth phase, with an expanding APAC footprint.

Work flexibly: Remote, outcome-driven working arrangements.

Benefits & Perks

• Salesforce, MuleSoft, and Project Management certifications paid for by J4RVIS.

• 2 days of study leave per quarter.

• Work From Anywhere for up to 2 weeks a year.

• Work from Home working arrangements.

• Healthcare Benefit (Includes 2 Dependents).

• J4You Rewards (Work Anniversary Reward).

• Home office expense allowance.

• Work with Salesforce, MuleSoft, AI, and Data experts.

• Clear career growth opportunities within a rapidly transforming AI-native consultancy.

Apply now

If you’re strong in Snowflake and dbt, enjoy solving complex data engineering problems, and want to take genuine ownership of your work from design through production, we’d love to hear from you.

Find out more about J4RVIS at AI-native consulting firm Australia | J4RVIS