# Engineering Manager - Data
**Company:** [Xero](https://scaleengineer.com/companies/xero)
As an Engineering Manager - Data at Xero, you will lead a distributed team of nine senior engineers across Australia, New Zealand, and Canada responsible for designing, building, and maintaining core models on Xero's data platform. This role combines strategic leadership with hands-on technical expertise, requiring you to balance platform architecture decisions with delivery execution while coaching senior talent and establishing operational excellence standards. You'll partner with product, platform, and security teams to deliver scalable, high-quality data products that solve meaningful customer problems in Xero's cloud-based accounting ecosystem.
**Role:** Engineering Manager
**Seniority:** Manager
**Locations:** AU: Melbourne: (260 Burwood Rd)
**Salary:** 120000–180000 AUD
[Apply](https://jobs.ashbyhq.com/xero/138b9018-6c2a-47cc-93f0-53545fa604a9)
Canonical: https://scaleengineer.com/jobs/xero/engineering-manager-data
---
## Responsibilities

- Lead Data Platform Architecture & Core Model Development: Design and build core data models on Xero's data platform using AWS and Snowflake infrastructure. Establish architectural standards and best practices for data modeling, ensuring scalability and performance across the platform. Make critical technical decisions that impact platform stability and evolution while mentoring engineers in advanced data architecture patterns.
- Manage Real-Time Data Flows & Streaming Architecture: Streamline real-time data flows and optimize streaming architecture across platform services. Lead initiatives to improve data latency, throughput, and reliability. Oversee the adoption of streaming technologies and ensure alignment with broader data platform strategy to support Xero's accounting products.
- Execute Complex Data Platform Initiatives: Drive delivery execution for complex, cross-functional data platform projects while managing dependencies, risks, and quarterly milestones. Coordinate with product, platform, and security teams to ensure timely, high-quality delivery of data products. Establish clear communication channels and escalation paths for dependency management.
- Coach & Develop Senior Engineering Talent: Mentor and elevate the technical capabilities of senior engineers on your team. Provide constructive feedback, support career progression, and foster an environment grounded in knowledge sharing and empathy. Set clear expectations, establish individual development plans, and create opportunities for engineers to lead complex initiatives.
- Establish Operational Excellence & DataOps Practices: Lead adoption of DataOps practices and establish operational standards across the team. Define and monitor Service Level Objectives (SLOs), manage platform health metrics, and drive continuous improvement in operational performance. Create systems and processes that enable predictable, reliable data platform operations.
- Build Cross-Functional Relationships & Collaborate: Establish trusted partnerships with product management, platform engineering, and security teams. Facilitate seamless collaboration across distributed team members in Australia, New Zealand, and Canada. Maintain alignment on strategic initiatives while balancing competing priorities and advocating for data platform needs.

## Requirements

### education

- {"name":"Bachelor's Degree in Computer Science or Related Field","description":"Formal education in Computer Science, Software Engineering, Data Science, or equivalent technical discipline. This foundation provides theoretical knowledge in algorithms, data structures, and systems design principles essential for platform architecture."}
- {"name":"Continuous Learning in Data Technologies","description":"Commitment to ongoing professional development in evolving data technologies, cloud platforms, and engineering leadership practices. Industry certifications in AWS or Snowflake are beneficial but not required; demonstrated self-directed learning through courses, publications, or conference participation is valued."}

### technical

- {"name":"Data Platform Architecture & Design","description":"Deep expertise in designing scalable data platform architectures with proven experience leading data platform delivery initiatives. Understand data pipeline orchestration, data modeling paradigms (dimensional modeling, fact tables, slowly changing dimensions), and platform health considerations."}
- {"name":"Cloud Infrastructure Proficiency","description":"Practical, hands-on familiarity with AWS services (EC2, S3, Lambda, RDS, Glue, Athena) and Snowflake data warehouse platform. Experience architecting cloud-native data solutions, managing infrastructure costs, and implementing security controls in cloud environments."}
- {"name":"Real-Time Data & Streaming Technologies","description":"Understanding of real-time data flows, streaming architecture patterns, and technologies like Apache Kafka, AWS Kinesis, or Spark Streaming. Experience optimizing data latency and throughput while maintaining data quality in streaming contexts."}
- {"name":"DataOps & Operational Excellence","description":"Demonstrated ability to establish Service Level Objectives (SLOs), monitor data platform health, and implement operational metrics. Experience implementing monitoring, alerting, and incident response for data systems. Knowledge of data governance, quality frameworks, and data lineage tracking."}
- {"name":"Data Modeling & Analytics Engineering","description":"Strong foundation in data modeling techniques, ETL/ELT pipeline design, and analytical database optimization. Experience working with SQL, business intelligence tools, and translating business requirements into robust data models that enable analytics and reporting."}

### experience

- {"name":"Data Platform Delivery Leadership","description":"5+ years of experience in data engineering or data platform roles, with 2+ years in a leadership capacity managing engineering teams. Proven track record delivering complex data infrastructure projects from conception through production, with demonstrated ability to balance strategic decisions with hands-on technical work."}
- {"name":"Senior Engineer Coaching & Development","description":"Demonstrated experience coaching and mentoring senior-level engineers in previous roles. Ability to set clear expectations, provide constructive feedback, and create development pathways for high-performing technical talent. Experience building high-performing, collaborative engineering teams."}
- {"name":"Cross-Functional Collaboration at Scale","description":"Experience working in distributed teams across multiple time zones or geographic regions. Proven ability to build trusted relationships with product, platform, and security stakeholders. Track record of managing dependencies and aligning cross-functional priorities in complex organizational environments."}
- {"name":"Risk & Dependency Management","description":"Experience managing technical risks, architectural trade-offs, and project dependencies in production data environments. Ability to identify blockers early, communicate escalations clearly, and drive resolution through collaborative problem-solving."}

## Skills

### required

- {"name":"AWS Cloud Platform","description":"Hands-on expertise with AWS data services including S3, Glue, Athena, Lambda, and RDS. Ability to architect cost-effective, scalable cloud solutions and implement security best practices in AWS environments."}
- {"name":"Snowflake Data Warehouse","description":"Production experience with Snowflake architecture, query optimization, clustering strategies, and cost management. Understanding of Snowflake's unique features for scaling analytics workloads and integrating with data pipelines."}
- {"name":"SQL & Data Querying","description":"Expert-level SQL proficiency for complex query writing, optimization, and analysis. Ability to write efficient queries for large-scale data processing and troubleshoot performance issues."}
- {"name":"Data Pipeline & ETL/ELT Design","description":"Strong experience designing robust ETL/ELT pipelines, orchestrating workflows, and managing data dependencies. Familiarity with orchestration tools like Apache Airflow, dbt (data build tool), or similar frameworks."}
- {"name":"Engineering Leadership & Team Management","description":"Proven capability in technical leadership, including hiring, onboarding, performance management, and career development of engineering teams. Ability to establish team standards, conduct technical reviews, and foster psychological safety."}
- {"name":"Real-Time Streaming Technologies","description":"Practical understanding of streaming data architectures, event-driven systems, and tools like Apache Kafka or AWS Kinesis. Experience designing systems that handle continuous data ingestion while maintaining quality and freshness."}
- {"name":"Service Level Objectives & Monitoring","description":"Experience defining SLOs, implementing observability solutions, and establishing on-call practices for mission-critical systems. Ability to balance reliability, performance, and cost objectives."}
- {"name":"Collaborative Communication & Cross-Functional Alignment","description":"Strong written and verbal communication skills with ability to translate between technical and non-technical stakeholders. Experience facilitating discussions, managing competing priorities, and building consensus across teams."}

### preferred

- {"name":"Experience with dbt (Data Build Tool)","description":"Familiarity with modern data transformation frameworks like dbt, including version control integration, testing frameworks, and documentation generation for analytics engineering."}
- {"name":"Data Governance & Metadata Management","description":"Experience implementing data governance frameworks, data catalogs, and metadata management solutions. Understanding of data lineage, data quality frameworks, and regulatory compliance considerations."}
- {"name":"Distributed Systems & Scalability Patterns","description":"Knowledge of distributed systems concepts, horizontal and vertical scaling strategies, and architectural patterns for large-scale data processing (MapReduce, partitioning, sharding)."}
- {"name":"Python or Scala for Data Engineering","description":"Programming experience in Python or Scala for building data processing applications, particularly with frameworks like PySpark or Scala Spark for distributed data processing."}
- {"name":"SaaS Product & Accounting Domain Knowledge","description":"Experience with SaaS platforms, accounting software, or fintech environments. Understanding of accounting principles, financial reporting, and domain-specific data challenges is a plus."}
- {"name":"Multi-Site & Distributed Team Leadership","description":"Experience managing teams across multiple locations or time zones. Demonstrated ability to maintain team cohesion, productivity, and culture in distributed environments."}

## Tech stack

### tools

- {"name":"AWS Glue","description":"AWS managed ETL service for data integration, cataloging, and transformation. Simplifies data pipeline development and reduces infrastructure management overhead."}
- {"name":"AWS Kinesis","description":"Real-time data streaming service for ingesting, processing, and analyzing data streams. Core to building real-time data platforms and streaming architectures."}
- {"name":"Datadog or New Relic","description":"Observability platforms for monitoring data pipeline health, performance metrics, and system reliability. Essential for establishing SLOs and incident response."}
- {"name":"Git & GitHub","description":"Version control system for managing infrastructure-as-code, data pipeline configurations, and collaborative development. Essential for modern DataOps practices."}
- {"name":"Jira or Linear","description":"Project management and issue tracking tools for managing sprint planning, dependency tracking, and cross-team coordination in agile environments."}

### others

- {"name":"Infrastructure-as-Code (IaC)","description":"Experience with Terraform, CloudFormation, or similar tools for defining and managing cloud infrastructure. Essential for reproducible, scalable data platform deployment."}
- {"name":"Container Orchestration","description":"Docker and Kubernetes experience for containerizing data applications and services. Relevant for modern data platform architectures and microservices integration."}
- {"name":"Data Quality & Testing Frameworks","description":"Familiarity with data quality tools (Great Expectations, Soda), testing frameworks for data pipelines, and quality assurance best practices in data systems."}
- {"name":"API Design & REST Services","description":"Understanding of API design principles for data platform services, enabling clean integrations with product teams and platform consumers."}

### databases

- {"name":"Snowflake","description":"Cloud-native data warehouse serving as the central repository for analytics, reporting, and data products. Requires expertise in architecture, query optimization, and cost management."}
- {"name":"Amazon S3","description":"Cloud object storage service used as a data lake for raw data ingestion and archival. Essential for building cost-effective, scalable data platforms on AWS."}
- {"name":"Amazon RDS","description":"Relational database service supporting operational data stores and application databases. Integration with data platform for source system connectivity and real-time data capture."}
- {"name":"Amazon DynamoDB","description":"NoSQL database for managing operational data and supporting real-time access patterns. Used in data pipeline design for handling high-velocity data ingestion."}

### languages

- {"name":"SQL","description":"Core language for data platform development, used extensively for querying, optimization, and analysis across Snowflake and AWS data warehouses."}
- {"name":"Python","description":"Widely used for building data pipelines, data quality checks, and orchestration scripts. Essential for data engineering and operational automation tasks."}
- {"name":"Scala (Optional)","description":"Used with Apache Spark for distributed data processing and transformation at scale, particularly for handling large data volumes efficiently."}

### frameworks

- {"name":"Apache Airflow","description":"Workflow orchestration and scheduling platform for managing complex data pipeline dependencies and monitoring. Core to reliable, reproducible data platform operations."}
- {"name":"dbt (Data Build Tool)","description":"Modern data transformation framework enabling version control, testing, and documentation of data models. Enhances data modeling practices and collaboration across analytics engineering."}
- {"name":"Apache Spark","description":"Distributed data processing framework used for large-scale ETL/ELT transformations, particularly with PySpark or Scala for complex data processing logic."}
- {"name":"Apache Kafka","description":"Event streaming platform for building real-time data pipelines and managing data flows across distributed systems. Core to streaming architecture initiatives."}

## Benefits

### benefits

- {"name":"Flexible Hybrid Work Arrangements","description":"Balance remote work with designated office boost days in Sydney or Melbourne. Flexibility to collaborate in-person while maintaining work-life balance and reducing commute times."}
- {"name":"Distributed Team Collaboration","description":"Work across a global distributed team spanning Australia, New Zealand, and Canada. Access to diverse perspectives and asynchronous work patterns that enable focused, deep work."}
- {"name":"Technical Leadership Development","description":"Opportunity to lead a high-performing team of nine senior engineers, directly shaping engineering culture, standards, and practices. Support from Xero's leadership development programs and coaching resources."}
- {"name":"Impact on Scalable Data Products","description":"Direct ownership of core data models and platform architecture that serve Xero's global customer base. Your technical decisions will influence product capabilities and customer experiences at scale."}
- {"name":"Strategic & Hands-On Engagement","description":"Unique opportunity to balance strategic leadership with hands-on technical contribution. Step in to mentor, guide architecture decisions, and participate in critical data platform initiatives."}
- {"name":"Cross-Functional Partnerships","description":"Collaborate closely with product, platform, and security teams. Engage in strategic initiatives that shape Xero's data ecosystem and technical direction."}

## Compensation

- **max:** 180000
- **min:** 120000
- **currency:** AUD
- **stockOptions:** true

## Interview process

### steps

## Full description
**The role / impact**

As our 'Engineering Manager - Data', you will lead the engineering team responsible for creating and maintaining core models on our data platform. In this role, you will balance strategic leadership with technical delivery, shaping platform architecture while staying close enough to execution to ensure delivery, stability, and quality standards are met.

You will empower and grow senior engineering talent, establish operational standards, and manage risk and dependencies across key quarterly initiatives. Partnering closely with product, platform, and security teams, you will ensure high-quality, scalable data products are delivered to solve meaningful customer problems.

**The team & how they connect**

The 'Dataverse' team consists of nine engineers located across Australia, New Zealand, and Canada who build and maintain the core models in our data platform. Working across remote locations, the team collaborates closely to manage cross-team dependencies and maintain continuous alignment. They foster an environment grounded in knowledge sharing, constructive feedback, and empathy for stakeholders.

**Initially, the role may focus on**

* Designing and building core models on the data platform using AWS and Snowflake infrastructure.
* Streamlining real-time data flows and streaming architecture across platform services.
* Leading delivery execution for complex data platform initiatives while managing dependencies and risks.
* Coaching senior engineers, elevating engineering standards, and supporting team adoption of DataOps practices.

**Where and how you can work**

This role offers flexible hybrid working arrangements based out of our Sydney or Melbourne offices. You can balance remote work with designated office boost days to connect, whiteboard, and collaborate in person with your team.

**Here are some of the things we're looking for, for this role:**

* A background in data platform delivery alongside data engineering expertise and data modelling capability.
* Practical familiarity with cloud infrastructure like AWS and Snowflake, along with an understanding of real-time streaming technologies and data flows.
* Your leadership style combines delivery focus with the technical breadth required to guide architectural decisions and step in hands-on when required.
* Proven capability in coaching senior engineers, setting clear expectations, and supporting individual career progression.
* Demonstrated proficiency managing platform health, establishing Service Level Objectives, and leading operational performance.
* Strong collaborative skills to build trusted relationships across product, platform, and security teams.

**Apply even if your experience isn't a perfect match! At Xero, we hire based on your skills, passion, and the unique perspective you can bring to enhance our culture and team.**