📊 Senior Data Engineer at Equiem Holdings Ltd

, , Australia -

Full Time

Start Date

Immediate

Expiry Date

20 Sep, 26

Salary

0.0

Posted On

22 Jun, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Pipeline Design, SQL, Dbt, AWS Glue, Python, TypeScript, AWS CDK, Amazon Kinesis, Amazon S3, Amazon Athena, Data Modeling, ETL, Node.js, OpenSearch, Data Quality Testing, Schema Migration

Industry

Real Estate

Description

✨ About the Role Equiem is an established global technology company operating within the real estate sector. We build products that transform the tenant experience and streamline property management. Our teams are cross-functional, made up of engineers, product managers, and designers, working together to deliver impactful solutions. As a Senior Data Engineer you'll own meaningful chunks of our data platform end-to-end designing pipelines, making architectural decisions, evolving data models, and working closely with product and analytics teams. We're building something ambitious: a data warehouse that powers reporting, BI, and AI-powered features. You care about data quality, pipeline correctness, cost efficiency, and building things the next engineer can maintain and extend. 🎯 What You'll Do * Design, build, and maintain data pipeline components spanning ingestion, streaming (Kinesis), storage (S3), and transformation (dbt, Glue). * Lead or contribute to data transformation layer re-architecture a rare opportunity to shape how data flows across an entire suite. * Build and maintain Glue ETL jobs and evolve dbt model layers (staging, intermediate, mart). * Ensure pipeline correctness with exactly-once delivery, deduplication logic, and schema migration management. * Implement data quality assertions and monitor pipeline health proactively. * Partner closely with product managers, analysts, and customer success to translate needs into well-modeled dbt marts. 🔍 What We're Looking For Essential: • Strong hands-on experience building and operating production data pipelines ideally event-driven or streaming. • Solid SQL skills and meaningful dbt experience. • Familiarity with AWS data services: S3, Athena, Glue, Lambda, Kinesis, SQS, SNS, Opensearch. • Proficiency in Python or TypeScript/Node.js for pipeline code. • Experience with AWS CDK. • Strong instinct for data quality: you write tests and treat unvalidated data as a bug. • Clear communication about schema decisions and pipeline tradeoffs. 🛠️ Our Data Stack * Runtime & Language: Node.js 24, TypeScript (ESM). * Ingestion: Amazon API Gateway (Edge-optimised) → AWS Lambda → Amazon Kinesis Data Streams. * Storage: Amazon S3, OpenSearch. * Transformation: AWS Glue, dbt on Athena (dbt-athena-community adapter). * Query Engine: Amazon Athena (serverless, pay-per-scan). * BI & Visualisation: Metabase, Amazon QuickSight. * Infrastructure as Code: AWS CDK (TypeScript). * CI/CD: CircleCI for scheduled dbt runs and CI. * Data Quality: dbt tests and assertions, schema migration management. Nice to Have: • Experience migrating or re-architecting a data warehouse • Exposure to PII handling and compliance frameworks (SOC2, GDPR) • Experience building customer-facing data export or API products • Background in B2B SaaS analytics or PropTech • Familiarity with Metabase, QuickSight, or similar BI tooling • Experience with parquet and columnar storage optimization 💚 Why You'll Love It Here 7 years of untapped event data waiting to be unlocked A real FY27 mandate with funded priorities Data Warehouse v2, shared data model, bulk export API Complete, modern serverless stack Kinesis, Lambda, S3, Athena, Glue, dbt, OpenSearch, CDK Work that drives real business outcomes for customers and internal teams Clear path from Senior to Lead Data Engineer with opportunity to shape Equiem's data engineering discipline Flexible remote work, wellbeing leave, paid parental leave, EAP, leadership development Join the data revolution apply today! 📈

Responsibilities

Design, build, and maintain end-to-end data pipelines and transformation layers using AWS services and dbt. Collaborate with product and analytics teams to evolve data models and ensure high data quality and pipeline correctness.