Data Engineer (Data Warehouse & ETL Platform)
About Euka
Euka Future Learning is Australia's leading home education platform, delivering K-12 curriculum to thousands of families across the country. Our engineering team builds the systems that power enrolments, billing, reporting, and learning. We have built a finance-grade revenue warehouse on Postgres and dbt with deploy-gated data tests, and we are now expanding the same platform to serve marketing attribution, product analytics, and AI-powered data products.
We are looking for a hands-on Data Engineer who can own this platform: the pipelines, the infrastructure, and the numbers they produce.
What You'll Work On
Current Stack (all live in production)
Ingestion: Airbyte (application DB connector), Stripe Data Pipeline (full snapshots to S3 as parquet), Python sync jobs, S3 data lake
Billing sources: Chargebee (subscriptions, invoices, credit notes), Stripe (charges, refunds, chargebacks)
ETL source DB: MySQL application mirror (prod_portaletl)
Data Warehouse: Postgres (euka_dw), dbt with staging, intermediate, and finance mart layers and a large deploy-gated data test suite
Infrastructure: AWS (S3, EC2, RDS, IAM, SSM), infrastructure as code with AWS CDK
CI/CD: GitHub Actions deploy pipeline; a merge to master runs the full ELT and blocks on failed dbt tests
BI / Reporting: Grow.com finance and operations dashboards
Monitoring: Datadog
Search / Operational: Meilisearch
Where We're Heading (Phase 2+)
Marketing attribution and media mix modelling on the same warehouse
Data foundation for AI products: well-structured, consistently typed pipeline outputs that LLM-based features can consume safely
Postgres remains our warehouse; we will evaluate Snowflake if and when we hit its limits
Key Responsibilities
Data Warehouse & ELT Platform Ownership
Own the ELT platform end to end: Airbyte connectors, S3 ingestion, Python sync jobs, dbt models, and the deploy pipeline that gates on data tests
Make infrastructure changes through code: AWS CDK stacks for buckets, runners, databases, IAM with least privilege
Provision and manage databases and access: Postgres roles, approval-gated access patterns, credentials via Secrets Manager
Keep the deploy honest: a green pipeline must mean correct, fresh data; failures must fail loudly
Revenue Data Integrity (Phase 1 anchor)
Own ingestion, transformation, and reconciliation of Chargebee and Stripe data into the finance marts: accrual spreads, refund handling, GST splits, bad debt conventions
Reconcile to the cent across billing, payments, and warehouse; respond to CFO and finance requests with clear, business-facing answers
Triage and fix data integrity bugs: NULL foreign keys, accrual spread errors, missing transactions; write regression tests so each bug class can never silently return
SQL & Validation
Write complex multi-table SQL across MySQL and Postgres: joins across licences, subscriptions, invoices, and line items; window functions, CTEs, exact-count validation
Maintain reconciliation checks and Datadog monitors that surface drift before finance does
Document join maps, table semantics, and data quirks
What We're Looking For
Required
3+ years data engineering or analytics engineering experience
Strong SQL: complex multi-table joins, CTEs, window functions in MySQL and Postgres
dbt in production: modular models, data tests, mart modelling
ELT orchestration: Airbyte, Airflow, Dagster, Fivetran, or similar in production
Infrastructure as code: AWS CDK, Terraform, or CloudFormation (we use CDK in TypeScript); comfortable owning infra changes, not just requesting them
AWS fundamentals: S3, EC2 or ECS, RDS, IAM, Secrets Manager, SSM
Billing/payments data: Chargebee and Stripe, or equivalent subscription billing and payment platforms, including reconciliation between them
Debugging instinct: can trace a revenue discrepancy across source, pipeline, and warehouse in one sitting
Accrual accounting concepts: accrual period, cash vs accrual timing, refund recognition
Highly Regarded
Kubernetes, ArgoCD, or similar platform operations experience
FinOps: cloud cost visibility and reduction
Datadog for pipeline monitoring and alerting
Practical LLM exposure in a data context (RAG over structured data, eval harnesses, guarding against hallucinated numbers) and a clear-eyed view of what is production-ready
Grow.com or similar BI tools; Meilisearch or similar operational search
SaaS subscription / EdTech background
Tech Stack: Now & Next
Ingestion: Airbyte + S3 snapshots → same, expanded sources
Warehouse: Postgres (euka_dw) → Postgres (Snowflake only if we hit Postgres limits)
Transformations: dbt (live) → dbt, expanded coverage
Infrastructure: AWS CDK → AWS CDK
CI/CD: GitHub Actions + SSM deploys → same, hardened
BI / Reporting: Grow.com → Grow.com
Search / Operational: Meilisearch → Meilisearch
AI / LLM: data foundation work → selective production builds
Monitoring: Datadog → Datadog
Project Management: Jira
Who You Are
Precise: you know "approximately right" is not good enough when the CFO is asking about January revenue
A platform owner: you fix the pipeline AND the deploy process that let the bug ship
Investigative: you enjoy tracing a bug through 5 layers until you find the broken join or the NULL that shouldn't be there
Pragmatic about AI: you use AI tools to move faster and you know the difference between a well-grounded use case and a hallucination risk in financial data
Clear communicator: you translate data findings into plain language for finance and executives with no engineering jargon
Autonomous: you operate well in a remote-first, async team without hand-holding
Location & Logistics
Remote: Australia preferred (AEST working hours)
Full-time/Contractor
How to Apply
Send your CV to [email protected] with the subject-line: 2026 AUGUST I'm ready to change education with data. Please add a short note covering:
A revenue reconciliation or pipeline bug you’ve solved
An infrastructure or pipeline change you shipped with IaC (CDK, Terraform, or similar): what it was, what could have gone wrong, and how you made it safe
Any hands-on LLM/AI work in a data context (optional)
Salary expectations