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Senior Data Delivery Engineer
BTI Executive Search —Australia
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- Azure
- SQL
- AWS

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Senior Data Delivery Engineer

BTI Executive Search
Australia

Apply Now

Job Details

Full-time

Qualifications

CI/CD
Data modelling
Azure
Git
Google Cloud Platform
SQL
AWS
PostgreSQL
Continuous integration
Data warehouse

Full Job Description

Australia

Full time

Position: Senior Data Delivery Engineer
Mode: Fully remote

About the role:
You will own and operate our dbt transformation layer that converts raw transaction data into clean, consistent, and trustworthy datasets for internal products and client delivery. This role is hands-on: you will write and maintain dbt models and rules, implement automated validation, monitor data quality, and troubleshoot issues quickly as upstream data and business logic change.

Responsibilities:

dbt-first transformation ownership (models, tests, documentation)
Receipt data cleaning rules (normalisation, standardisation, edge cases)
Data integrity (quality gates, monitoring, incident response)

Problem space
Receipt data is inherently messy. You will routinely work through:

Retailer and format variability, missing/ambiguous fields, and inconsistent line items
Edge cases and exceptions where rules must be explicit and versioned
Schema drift and upstream changes that can silently break assumptions
Data quality incidents (freshness/completeness/correctness) requiring fast triage and durable fixes

Skills & experience
Must-have

Strong SQL and practical data modelling skills (staging marts / delivery outputs).
Production experience with dbt (models, tests, docs; comfortable with refactors).
Solid understanding of data warehousing and ELT/ETL concepts.
Experience working with a cloud data platform (AWS, GCP, and/or Azure).
Strong problem-solving and debugging skills; high attention to detail.
Clear communication and ability to collaborate across product/ops/engineering.

Nice-to-have

Python for data tooling/automation and future pipeline work.
Experience with messy transactional datasets (e.g., receipts), schema drift, or semi-structured sources.
Data observability/quality tooling experience (custom monitoring, Great Expectations, or similar).
Orchestration and CI/CD exposure (Airflow/Dagster/Prefect; PR-based release workflows).

Responsibilities:
1) Own dbt models and cleaning rules

Build, maintain, and improve dbt models that clean and standardise receipt data.
Translate business rules into durable transformation logic (including handling edge cases and retailer variability).
Keep the project maintainable through refactoring, consistent patterns, and clear documentation.

2) Data quality, validation, and integrity

Implement and maintain automated tests (schema, uniqueness, not-null, relationships, accepted values) and custom tests where required.
Define quality expectations for key outputs and ensure failures are caught early.
Investigate anomalies, identify root cause (source vs model vs rule), and implement durable fixes.

3) Monitoring and operational reliability

Monitor scheduled runs and downstream outputs (freshness, completeness, key metric sanity checks).
Improve observability and incident response: alerts, dashboards where appropriate, and runbooks for common failure modes.
Reduce operational toil by addressing recurring issues systematically.

4) Collaboration and delivery alignment

Partner with stakeholders to clarify requirements and ensure datasets meet delivery needs.
Communicate trade-offs clearly (accuracy vs coverage vs latency vs complexity).
Review contributions to the dbt project and help raise engineering quality across the team.

5) Performance and cost discipline

Optimise model performance and warehouse usage (incremental strategies where appropriate, efficient joins, reduced scans).
Balance correctness, coverage, and cost constraints.

6) Data ethics and privacy

Follow data privacy and responsible handling requirements.
Identify potential compliance risks and escalate or propose mitigations.

Tools (working environment)

Snowflake, PostgreSQL
Git-based workflow (PRs, reviews, CI)
Cloud platform exposure (AWS preferred)

Consultant

[email protected]

Reference number:

579740

Profession:

Company:

BTI Executive Search

Date posted:

18th Jul, 2026

Apply Now

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