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Toku is seeking an experienced Data Engineer to lead the design and implementation of robust, scalable, and maintainable data solutions. You will take ownership of the company's data architecture and infrastructure, designing and evolving systems that support complex data flows across multiple sources and volumes.
Key responsibilities include:
- Leading the design and implementation of data solutions, providing technical vision and guidance
- Designing and maintaining data warehouses, data lakes, and data layers used reliably across teams
- Building and maintaining ETL/ELT pipelines that integrate multiple data sources with varying complexity
- Making architectural trade-off decisions considering scalability, performance, costs, and maintainability
- Identifying risks and improvement opportunities, anticipating future challenges
- Working directly with stakeholders to translate business problems into technical solutions
- Ensuring data quality, availability, and reliability
- Collaborating with engineers on design, implementation, testing, and deployment
- Contributing to team technical standards and participating in hiring and development of future team members
Essential qualifications:
- Proven experience solving complex Data Engineering problems
- Experience with data warehouses, data lakes, and modern data architectures
- Strong background building and maintaining ETL/ELT pipelines and data ingestion/transformation processes
- Cloud data solutions experience (Google Cloud Platform preferred; AWS or other ecosystems valued)
- Strong SQL and Python proficiency
- Experience with relational databases and understanding of design principles
- Ability to design technical solutions and evaluate trade-offs
- Strong communication skills and ability to work with technical and non-technical stakeholders
- Technical curiosity and demonstrated ability to learn new tools and technologies independently
Desirable experience includes:
- Designing or evolving data platforms from scratch or modernizing/migrating existing systems
- Technologies such as BigQuery, Snowflake, Databricks, Airflow, dbt, Spark, or Kafka
- Working with large data volumes and solving scalability, performance, or cost optimization challenges
- Data replication, CDC, batch and/or streaming processing
- Data modeling, data governance, observability, and data quality
- Git and software development best practices
- Technical ownership of end-to-end data projects or initiatives
The company values curious, proactive individuals who adapt quickly to change, think creatively, enjoy teamwork, and aren't afraid to experiment and challenge the status quo.
