Senior Data Engineer

About Halcyon Labs Halcyon Labs works with top enterprises and digital native businesses in Australia helping them build their remote teams in India, Philippines and Sri Lanka. We pride ourselves with hiring great teams to work on exciting game changing technology. Halcyon Labs currently has 200+ engineers, testers, product managers etc. working across 20+ partners. We focus on uplifting the way organizations build and manage their remote teams through great working relationships, trust, integrity, culture and hiring processes. Additional to this we are building our own technology product offerings. We strive to deliver the best outcomes for our customers, our partners and our people. About Our Client Our client is a leading fintech company that helps businesses simplify complex payment workflows through a secure, API-driven payment platform. Their solutions enable organizations to collect, manage, reconcile, and automate payments at scale, supporting everything from payment orchestration and payouts to fraud management and embedded financial services. They work with mid-market and enterprise businesses across a range of industries to create seamless and efficient payment experiences. 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 organization. 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, optimizing 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. Key Responsibilities Data Platform & Engineering Design, build, and maintain scalable and secure cloud-native data platforms and data pipelines. Lead the architecture, optimization, 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. Optimize performance, scalability, and cost efficiency across data storage, transformation, and query workloads. Support near real-time and batch-based data processing requirements. Snowflake Engineering & Optimization Own and continuously improve Snowflake architecture, performance tuning, security, governance, and operational best practices. Design and optimize 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 optimization 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 optimize 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 pract