Senior Data Reliability Engineer

We apply SRE principles to everything we build — including our data platform. This role owns the reliability, quality, and scalability of our internal data ecosystem, bringing the same rigour (SLIs/SLOs, observability, incident response, automation) that traditional SREs apply to application services, but focused entirely on data systems. As our Senior Data Reliability Engineer, you will design data models, build transformation pipelines, create dashboards, and enable self-service analytics — all with production-grade reliability baked in from the start. You will be the person who ensures our data is trustworthy, timely, and accessible, and that we know immediately when it isn’t. We have early-stage foundations in place: some pipelines and tooling exist, but they’re fragmented and need consolidation, proper architecture, and the reliability engineering mindset this role will bring. Our stack is Azure-centric (Azure Data Factory, Synapse Analytics, Azure SQL, Power BI). 

This role sits within our SRE culture but is focused specifically on data systems. You’ll contribute to reliability practices across the organisation, but your day-to-day domain is the data platform — not application infrastructure. We have a separate platform team responsible for Kubernetes, service mesh, and application deployment pipelines.  We’re looking for someone who understands what the data means, not just the infrastructure it runs on.

What Success Looks Like 

In your first year, you will: 

• Consolidate fragmented data sources into a well-modelled, reliable data platform on Azure 

• Build production-grade ETL/ELT pipelines with monitoring, alerting, and automated recovery — not just pipelines that work, but pipelines that tell you when they don’t 

• Create Power BI dashboards and reports that give stakeholders operational visibility they can trust 

• Define and enforce SLOs for data freshness, accuracy, and availability — treating data as a service with clear reliability targets 

• Enable self-service analytics so teams can answer their own questions without waiting on you 

• Establish data quality standards and automated testing that catch issues before they reach reports 

• Reduce manual reporting overhead through automation, applying toil-reduction principles from SRE to data operations 

What You Will Do 

Data Pipeline Reliability & Development 

• Design data models (dimensional, star schema, or appropriate patterns) that serve reporting and analytics needs 

• Build and maintain ETL/ELT pipelines using Azure Data Factory, integrating internal and external data sources 

• Write and optimise SQL transformations for performance, cost, and clarity 

• Apply reliability engineering to every pipeline: error handling, retry logic, dead-letter queues, and graceful degradation 

• Ensure data quality, integrity, and consistency through automated testing and validation 

• Troubleshoot and resolve pipeline failures and data inconsistencies with proper incident response 

Observability & Incident Response for Data Systems 

• Define SLIs/SLOs for data services: freshness (how recent), completeness (how much), accuracy (how correct) 

• Implement monitoring and alerting for pipeline health, data quality drift, and report refresh status 

• Build observability into the platform so problems surface before users notice them 

• Lead incident response for data-related issues — diagnose root causes and implement lasting fixes, not just restarts 

• Conduct blameless post-mortems and feed learnings back into platform improvements 

• Join the out-of-hours on-call rota (typically once every 3–4 weeks) once suitably trained 

Reporting & Analytics Enablement 

• Build and maintain Power BI dashboards and reports for operational and strategic decision-making 

• Partner with stakeholders to understand their questions and translate them into reliable data solutions 

• Design the semantic layer and data models within Power BI (DAX, relationships, row-level security) 

• Enable self-service analytics by providing clean, documented, well-governed data 

• Establish a reporting platform that scales reliably as the organisation grows 

DataOps & Automation 

• Automate repetitive data processes using Python, SQL, and Azure-native tooling 

• Implement CI/CD for data pipelines and reporting assets (version control, testing, deployment) 

• Apply toil-reduction thinking: if you’re doing it manually more than twice, automate it 

• Manage data infrastructure as code where appropriate (Terraform, Bicep, or ARM templates) 

• Build reusable frameworks that raise the reliability baseline across all data services 

Cross-Functional Collaboration 

• Act as the bridge between technical teams and business stakeholders 

• Contribute to SRE practices and reliability culture across the wider organisation 

• Work with teams to understand their data needs and deliver solutions that meet defined service levels 

• Promote data literacy and help non-technical colleagues become confident with self-service tools 

What You Will Bring 

• 5+ years in Data Engineering, Analytics Engineering, or data-focused SRE/DevOps roles 

• Strong SQL skills — you think in SQL and can write complex, performant queries and transformations 

• Hands-on experience with data modelling (dimensional modelling, star schemas, or equivalent patterns) 

• Production experience building and maintaining data pipelines (Azure Data Factory preferred, or similar orchestration tools) 

• Hands-on Power BI development: building reports, DAX measures, data models, and published dashboards 

• Python scripting for data transformation, automation, and pipeline development 

• Experience implementing monitoring and observability for data systems (not just application infrastructure) 

• Experience working with stakeholders to translate business needs into data solutions 

• Understanding of SRE principles: SLIs/SLOs, error budgets, incident response, blameless post-mortems 

• Familiarity with cloud data platforms (Azure preferred: Synapse Analytics, Azure SQL, Azure Data Lake) 

Preferred 

• Experience building or consolidating a data platform from fragmented sources 

• Familiarity with DataOps practices: CI/CD for data assets, automated testing, version-controlled pipelines 

• Experience with data quality frameworks (Great Expectations, dbt tests, or equivalent) 

• Knowledge of data governance and cataloguing practices 

• Infrastructure as Code for data infrastructure (Terraform, Bicep) 

• Experience with Docker and Kubernetes in data platform contexts 

• Familiarity with modern transformation tools (dbt or similar) 

• Familiarity with AI tooling and experience using it to enhance productivity 

Why You Will Love It Here!

• Your Future: Professional Development Reimbursement including access to SS&C University
• Work/Life Balance: Competitive holiday scheme
• Your Wellbeing: Competitive benefits designed to support the wellbeing of our staff
• Diversity & Inclusion: Committed to Welcoming, Celebrating and Thriving on Diversity
• Training: Hands-On, Team-Customised throughout your career

We encourage applications from people of all backgrounds to enable us to bring diverse perspectives to our thinking and conversation. It's important to us that we strive to have a workforce that is diverse in the widest sense.

Thank you for your interest in SS&C! If applicable, to further explore this opportunity, please apply directly with us through our Careers page on our corporate website @ www.ssctech.com/careers.