Data Engineer

Who We Are…

Founded in 2002, ELMO Software has evolved from a pioneer in HR technology to the definitive leader in workforce innovation. We are the Complete AI Workforce Platform, providing a seamless, end-to-end ecosystem for over 2,000 mid-sized organisations and one million users across Australia and New Zealand.

We don’t just manage workflows; we transform them. By integrating cutting-edge automation and ISO-certified security, ELMO empowers HR professionals to move beyond administration and lead at the executive table. Whether it’s streamlining the employee lifecycle or delivering real-time data insights, our platform is built to scale, adapt, and reimagine what’s possible for the modern workforce.

About the Role:

You’re a hands-on database expert with deep experience across PostgreSQL, MySQL, and Microsoft SQL Server on AWS RDS. You know how to tune a slow query, design schemas that scale, and recover gracefully when a migration goes wrong at 2 a.m. But you’ve also evolved beyond the traditional DBA role, embracing the modern data stack and data engineering practices.

Today, you're building data pipelines, working with Snowflake and dbt, writing Python, and leveraging AI-powered tooling to accelerate delivery and improve outcomes.

That’s exactly the profile we’re looking for.

At ELMO, you’ll be responsible for the reliability, performance, and evolution of our Snowflake data platform, dbt transformation layers, AWS RDS databases, and batch data pipelines. Together, these systems underpin a high-scale, multi-tenant SaaS platform serving more than one million users across Australia, New Zealand, and the UK.

As part of ELMO’s AI-Native engineering model, you’ll partner with AI agents—including Claude Code, GitHub Copilot, and multi-agent workflows—to deliver complex data initiatives such as schema migrations, pipeline modernisation, and data quality framework development. While AI accelerates execution, your deep database expertise will remain critical in identifying performance bottlenecks, security risks, and data integrity issues that automated tools alone can’t reliably detect.

What you will be doing…

Own pipeline code, DBT models, and SQL transformations end-to-end; uphold team-level quality standards and act as first reviewer for junior PRs

Direct AI agents through complex, multi-step data engineering tasks; contribute effective prompts and orchestration patterns to the shared team prompt library

Evaluate AI-generated pipeline code, SQL, and dbt models for correctness, performance, security, and architectural alignment before merge

Build and maintain high-performance Snowflake pipelines — including schema design, RLS policies, query optimisation, cost management, and warehouse sizing

Design, develop, and maintain dbt models, tests, macros, and packages at production scale

Administer and tune AWS RDS databases across Postgres, MySQL, and MS SQL — schema design, performance tuning, query optimisation, index management, and backup/recovery

Build and maintain AWS-based data infrastructure using S3, Glue, Lambda, Kinesis, and EMR; apply IaC practices (Terraform or CloudFormation)

Manage workflow orchestration using Apache Airflow or equivalent, including retry strategies, observability, and production-grade reliability patterns

Design and implement automated data quality monitoring and validation frameworks; own data quality incidents from detection through to resolution and postmortem

Own ISO 27001 evidence collection for team pipelines and data assets; implement RLS, masking, and data access controls for sensitive HR and employee data

Mentor junior Data Engineers through PR review, pairing, and structured feedback

Must haves:

Strong AWS RDS DBA experience across Postgres (Aurora), MySQL (Aurora), and MS SQL — schema design, query optimisation, index management, performance tuning, and backup/recovery

Solid data engineering experience, ideally with a DBA background who has expanded into the modern data stack (Snowflake, dbt, Python)

Deep, hands-on Snowflake experience — query optimisation, cost management, RLS, schema design, and performance tuning

Proficient with DBT (models, tests, macros, packages) at production scale

Strong Python scripting and advanced SQL for transformations, automation, and tooling

Hands-on with AWS data services (S3, Glue, Lambda, Kinesis, EMR) and IaC (Terraform or CloudFormation)

Demonstrated experience building tools or automations using AI — Claude Code, GitHub Copilot, multi-step prompting, or multi-agent orchestration

Strong workflow orchestration experience (Apache Airflow, AWS Step Functions, or equivalent)

Familiarity with ISO 27001 data security obligations and compliance-aware data engineering practices

Strong written and verbal communication; comfortable engaging with non-technical stakeholders across Analytics, Product, and the business

Nice to have:

Experience with Apache Kafka and Apache Flink — consumer/producer patterns, partition strategies, CDC, and schema evolution

Exposure to AI/ML data pipelines — embedding generation, vector store ingestion, Bedrock knowledge base preparation, or training data management

Industry certification: SnowPro Advanced: Data Engineer, AWS Data Analytics Specialty, dbt Advanced, or equivalent