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:

1. A revenue reconciliation or pipeline bug you’ve solved

2. 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

3. Any hands-on LLM/AI work in a data context (optional)

4. Salary expectations