About Teamified
Teamified 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. Teamified 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 practices, and platform strategy.
- Drive continuous improvement initiatives across data architecture, tooling, and delivery practices.
- Work cross-functionally to improve organizational 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 optimization 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 optimizing 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.
Key Technologies
- Snowflake
- SQL
- dbt
- Python
- AWS / Azure / GCP
- Airflow / Matillion / Fivetran
- Looker / Power BI
- Git
- Kafka / Kinesis (desirable)
Success in the Role Looks Like
- The Snowflake platform is scalable, performant, secure, cost-efficient, and trusted across the organisation.
- Data pipelines and transformations are reliable, well-monitored, and highly automated.
- Stakeholders consistently rely on high-quality, trusted, and timely data for operational and strategic decision making.
- Data models, semantic layers, and reporting structures are well-designed, maintainable, and scalable.
- Data quality issues are proactively identified and resolved before impacting business outcomes.
- Engineering and analytics teams are empowered through reliable self-service data capabilities.
- Documentation for data pipelines, models, governance processes, and platform architecture is comprehensive and maintained.
- The broader organization experiences measurable improvements in data maturity, operational efficiency, and analytics capability.
- The data engineering function is recognized as a trusted technical and strategic partner across the business.
Benefits (PH):
- Flexibility in work hours and location, with a focus on managing energy rather than time.
- Access to online learning platforms and a budget for professional development
- A collaborative, no-silos environment, encouraging learning and growth across teams
- A dynamic social culture with team lunches, social events, and opportunities for creative input
- Health insurance
- Leave Benefits
- 13th Month Salary
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