Teamified
Senior Data Engineer
Vaga remota de Data Engineering com fit claro de localização do candidato.
Publicada4 de jul. de 2026
Países elegíveis2 países aceitos
Sinal de senioridadeSenior
Modelo de trabalhoRemoto
Locais aceitos para candidatos
ÍndiaFilipinas
Resumo da vaga
Senior Data Engineer
Requisitos e responsabilidades
Conteúdo da vaga extraído em seções para revisão mais rápida.
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.
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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