Senior Data Engineer (Snowflake / Redshift) | Dayshift | Remote

ZigZag is looking for a Sr Data Engineer to join our team!

About the role

We are seeking an experienced Senior Data Engineer to design, build, and scale modern cloud-based data platforms that enable trusted analytics, operational reporting, and data driven decision making across the organisation.

This role will play a critical part in evolving our data architecture, data engineering practices, and analytics capabilities, with a strong focus on Snowflake as the core enterprise data platform.

As a senior member of the data team, you will be responsible for building robust and scalable data pipelines, optimising data models, improving data quality and governance, and enabling self-service analytics capabilities for business and engineering stakeholders.

The ideal candidate combines strong technical depth in modern data engineering practices with a pragmatic mindset, excellent stakeholder engagement skills, and a passion for building reliable and scalable data platforms.

Data Platform & Engineering

• Design, build, and maintain scalable and secure cloud-native data platforms and data pipelines.

• Lead the architecture, optimisation, and operational management of the Snowflake data warehouse platform.

• Develop robust ELT/ETL pipelines to ingest, transform, and deliver high-quality data from multiple internal and external sources.

• Build reusable and maintainable data frameworks, transformation models, and orchestration workflows.

• Develop and maintain infrastructure-as-code and automation for data platform provisioning and management where appropriate.

• Optimise performance, scalability, and cost efficiency across data storage, transformation, and query workloads.

• Support near real-time and batch-based data processing requirements.

Snowflake Engineering & Optimisation

• Own and continuously improve Snowflake architecture, performance tuning, security, governance, and operational best practices.

• Design and optimise Snowflake schemas, warehouses, clustering strategies, and data sharing capabilities.

• Implement scalable data modelling approaches including dimensional modelling and data vault methodologies where appropriate.

• Manage Snowflake access controls, roles, permissions, and secure data sharing practices.

• Monitor Snowflake usage, query performance, and cost consumption to drive optimisation initiatives.

• Support data lifecycle management, retention, and governance policies within Snowflake.

Data Modelling & Analytics Enablement

• Design and maintain curated, trusted, and scalable data models to support analytics, reporting, and operational use cases.

• Partner with analysts, business stakeholders, and engineering teams to translate business requirements into scalable data solutions.

• Enable self-service analytics capabilities through well-structured semantic layers and governed datasets.

• Support and optimise BI and reporting platforms such as Looker, Power BI, or equivalent tools.

• Ensure data structures and models support both operational reporting and strategic analytics requirements.

Data Quality, Governance & Reliability

• Implement data quality controls, validation frameworks, reconciliation processes, and monitoring capabilities.

• Proactively identify and resolve data integrity, consistency, and performance issues.

• Establish observability and operational monitoring for data pipelines and platform reliability.

• Contribute to data governance, lineage, cataloguing, and metadata management practices.

• Ensure data platforms and engineering processes comply with security, privacy, and regulatory requirements.

Collaboration & Leadership

• Collaborate closely with Product, Engineering, Operations, Finance, and Business stakeholders to deliver impactful data solutions.

• Mentor and support junior engineers and analysts within the broader data function.

• Contribute to data engineering standards, best practices, and platform strategy.

• Drive continuous improvement initiatives across data architecture, tooling, and delivery practices.

• Work cross-functionally to improve organisational data literacy and data maturity.

Skills & Experience

Essential

• 5+ years of experience in Data Engineering, Analytics Engineering, or related data platform roles.

• Experience designing and supporting multi-region and enterprise-scale data platform architectures.

• Strong experience driving performance optimisation and cloud cost efficiency initiatives across large-scale data workloads.

• Strong understanding of platform reliability, operational maturity, resilience, and production support practices.

• Experience implementing advanced governance, security, access control, and data protection models within enterprise data platforms.

• Strong capability in developing architectural standards, engineering documentation, and scalable platform design patterns.

• Strong hands-on expertise with Snowflake in enterprise-scale environments.

• Advanced SQL skills with experience optimising complex analytical queries and data transformations.

• Strong experience building and maintaining modern ELT/ETL pipelines and orchestration workflows.

• Strong understanding of modern data warehousing concepts, dimensional modelling, and scalable data architecture.

• Experience working with cloud platforms such as AWS, Azure, or GCP.

• Experience with data transformation and orchestration tools such as dbt, Airflow, Fivetran, Matillion, or equivalent platforms.

• Experience integrating structured and semi-structured data sources.

• Strong understanding of data governance, security, and access management principles.

• Proven ability to manage large and complex datasets in production environments.

• Strong analytical, troubleshooting, and problem-solving capabilities.

• Excellent communication and stakeholder engagement skills.

• Ability to work effectively in fast-paced, agile, and collaborative environments.

Desirable

• Experience within fintech, payments, SaaS, or highly regulated industries.

• Experience with real-time data streaming technologies such as Kafka or Kinesis.

• Exposure to machine learning data pipelines and advanced analytics workloads.

• Experience implementing CI/CD practices for data engineering workflows.

• Familiarity with Infrastructure-as-Code tools such as Terraform.

• Experience with data observability and quality tooling.

• Exposure to compliance frameworks such as PCI-DSS, ISO27001, or SOC 2.

• Experience mentoring engineers or leading technical initiatives

ZigZag is committed to building a diverse, inclusive, and equitable workplace. We believe that talent knows no borders, and we welcome individuals from all backgrounds to help us shape the future of work. Guided by transparency and agility, we foster an environment where everyone is valued and empowered to thrive.

ZigZag is committed to building a diverse, inclusive, and equitable workplace. We believe that talent knows no borders, and we welcome individuals from all backgrounds to help us shape the future of work. Guided by transparency and agility, we foster an environment where everyone is valued and empowered to thrive.

By submitting this application, you acknowledge that you have read and agree with the company’s Privacy Policy.