Deputy16d ago

Data Management Analyst

SydneyFull Timemid

OtherManagement Analyst

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Overview

Deputy is a global SaaS workforce management company with hubs in Sydney, Melbourne, San Francisco and London, plus team members working remotely across the United States. Our platform serves over 1.

Technical Tools

OtherManagement Analyst

Deputy is a global SaaS workforce management company with hubs in Sydney, Melbourne, San Francisco and London, plus team members working remotely across the United States. Our platform serves over 1.5 million workers and 375,000 workplaces across 100+ countries. We are backed by top global investors and recently achieved Unicorn status.

At Deputy, we’re improving the world of work, one shift at a time, for 80% of the world’s workforce: hourly workers. These are the dedicated employees who keep our world running – from baristas to nurses, cleaners to delivery drivers, florists to factory workers. Despite their vital role in society, most workplace technology has focused on those workers who sit behind a desk, but at Deputy, we transform the frontline. When businesses use Deputy, their workplaces thrive – the business is more profitable, compliant, and productive, while the workers are more engaged and happier at work.

We're becoming an AI-native company, a commitment that means you'll be empowered (and expected) to use AI tools and thinking in your day-to-day work. You'll have the training, support, and freedom to use AI responsibly and creatively to spark ideas, solve problems faster, and unlock new ways of working.

If you’re passionate about creating solutions that put people first and helping businesses and their teams thrive, join us at Deputy and make an impact where it matters most!

As our Data Management Analyst, you will be the primary architect of Deputy’s Data Governance Framework, operating at the intersection of Data Engineering and Business Intelligence with the empowerment of AI. Aligned with DAMA DMBOK standards, you will lead the deployment of our metadata and governance platforms, infrastructure and processes. Your mission is to transform our data estate into a formal "Data Asset," ensuring structural integrity, high discoverability, and rigorous quality standards to fuel Deputy’s global scaling.

This role sits within the Data Team with a company-wide mandate to architect Data Governance with a special focus on Metadata Management, Data Quality and Data Risk. You will lead the implementation of our "Governance-as-Code" approach using an organisation-wide Data Governance platform (eg. Unity Catalog/Data Hub) and embedding AI-native processes/tooling. While you are not the perimeter security officer, you are the Data Custodian responsible for the identification, classification, and lifecycle management of sensitive information (PII/PHI). You will bridge the gap between raw data ingestion and curated, governed Data Products that the entire organization can trust.

You are a technical analyst who views the DAMA DMBOK Knowledge Areas—particularly Metadata, Quality, and Risk - as foundational engineering requirements. You believe in "Governance by Design," building automated guardrails within the transformation layer rather than manual gates at the finish line. You leverage Artificial Intelligence across people, process and technology where there is clear business value and strategic impact. You are motivated by turning "Dark Data" into searchable, high-utility assets and thrive in complex environments where you can bridge technical debt with scalable structural solutions.

Metadata Management & Discovery

Tool Orchestration: You are the Platform Owner for our Data Catalogue and Governance layer, ensuring seamless integration across the modern data stack and intelligent automation.

Taxonomy & Knowledge Graph: Design and maintain a universal Business Glossary and Technical Metadata schema. You will ensure that every asset has defined ownership, lineage, and semantic meaning. You will strategise how to scale and automate the underlying processes using AI (eg. AI-generated attribute definitions)

Data Discovery: Improve the "Time-to-Insight" by optimizing searchability and documentation, ensuring that downstream users can autonomously find and verify the data they need. You will leverage Agentic AI to enhance this autonomy with calculated risk of accuracy and consistency.

Data Quality & Analytics Engineering

Quality-as-Code: Partner with Analytics Engineers to embed dbt tests and automated quality assertions into our CI/CD pipelines, aligning with the DAMA Data Quality dimensions (Accuracy, Completeness, Consistency, etc.).

Lineage & Impact Analysis: Utilize our data catalogue to monitor end-to-end data lineage, performing proactive impact analysis to ensure upstream changes don't break downstream "Sources of Truth."

Reference Data Management: Standardize reference data sets across our tech stack to ensure "one version of the truth" for dimensions like Currency, Geography, and Billing Structure.

Data Risk, Privacy & Lifecycle Implementation

Data Classification: Operationalise PII/Sensitive data identification. You will implement automated tagging and Attribute-Based Access Control (ABAC) / Role-Based Access Control (RBAC) to mitigate risk without slowing down analysis.

Stewardship & Accountability: Facilitate Data Stewardship by partnering with department heads to enable Data Owners, establishing clear RACI models for data domains.

Lifecycle Execution: Platform enablement of data lifecycle management across landing, transformation, and access data layers.

Experience: 2–5 years in Data Governance, Analytics Engineering, or Data Management. Familiarity with DAMA DMBOK frameworks is highly preferred.

Technical Mastery: Advanced SQL and hands-on experience with dbt (Data Build Tool). You must be able to read and audit transformation logic.

Applied AI: Proven experience of architecting and implementing intelligent automation and agentic AI patterns in processes and tooling

Governance Architecture: Proven experience implementing enterprise-grade catalogs such as Databricks Unity Catalog or similar (Alation, Collibra, DataHub).

Risk & Privacy Fluency: A technical understanding of how to map GDPR, SOC2, and ISO 27001 requirements to physical data structures and access patterns.

SaaS Ecosystems: Experience managing data flowing through high-volume integration tools like Fivetran, Stitch, or Airbyte.

Preferred Tech Stack Experience:

Data Infrastructure: Databricks (Unity Catalog), AWS S3, Redshift.

Data Modeling: dbt (Core or Cloud), SQL.

Metadata/Governance: DataHub, Great Expectations, or Monte Carlo.

Domain Knowledge: Deep familiarity with Workforce Management data (HRIS, Payroll, and Core operational modules).

BI & Analytics: Looker, Thoughtspot, or Deputy Analytics+.