Senior Data Engineer
The Senior Data Engineer will design and maintain scalable data pipelines and lakehouse architecture using Databricks, Spark, and dbt. This role focuses on building robust data models and infrastructure to support analytics across Trust Wallet's global crypto platform.
- Compensation
- Not disclosed
- Location
- Remote - Global
- Work setup
- Remote
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 3+ years
- Category
- Data engineering
- Posted
Skills
Must have
- 3+ years of experience as a Data Engineer
- Hands-on ownership of production pipelines and lakehouse or data warehouse architecture
- Strong practical experience with Databricks, Delta Lake, and Spark
- Solid understanding of dimensional modelling, slowly changing dimensions, and data warehouse design
- Experience with dbt or an equivalent transformation framework
- Strong cloud fundamentals (AWS, Azure, or GCP)
- Proficiency in Python and SQL
- Experience implementing testing, monitoring, and governance practices
Nice to have
- Experience with change data capture and replication
- Familiarity with blockchain or on-chain data
- Infrastructure as code (Terraform) and CI/CD
- Experience with BI and semantic layers (Holistics, Looker)
- Containerisation and orchestration (Docker, Kubernetes)
Full description
About the company
Trust Wallet is the secure, self-custody Web3 wallet that empowers people to own and control their digital assets. Whether you're new to crypto or an experienced user, Trust Wallet makes it easier, safer, and more convenient to explore Web3, securely access dApps, manage your crypto, and buy and sell digital assets, all in one place.
About the Role
As a Senior Data Engineer, you will play a key role in building and scaling the data platform that powers analytics across Trust Wallet. You'll be responsible for designing, developing and maintaining data pipelines and data models, ensuring a seamless, scalable and reliable flow of data from source systems through to the decisions it supports. Leveraging your experience with Databricks and Spark across both streaming and batch workloads, dbt or a similar transformation framework, cloud infrastructure, and custom solutions in Python, you'll work closely with our data analysts and engineering teams to turn data into a strategic asset for our company.
Key Responsibilities
- Data Platform Engineering: Architect and maintain robust, scalable and secure data infrastructure on Databricks, covering both streaming and batch workloads.
- Data Pipeline Development: Design, develop and maintain data pipelines, primarily in Python and Spark, to automate ingestion and transformation across internal systems, external providers and on-chain sources.
- Data Modelling: Design and maintain dimensional data models and transformation layers in dbt, with tests and documented contracts, so metrics are consistent and reusable across the company.
- Data Lake Management: Oversee the data lake and lakehouse layers, ensuring efficient storage, effective partitioning, high data quality, and monitoring and alerting that surfaces issues early.
- Integration and Customisation: Integrate Databricks with a wide range of data sources, including change data capture from operational databases, third-party APIs and blockchain data, and adapt data flows to specific business needs.
- Performance, Scalability and Cost: Optimise pipelines and storage for performance, reliability and cost efficiency at scale.
- Data Governance and Security: Apply best practices for governance, security and compliance in cloud and Databricks environments, including access control, encryption and monitoring.
- Collaboration and Documentation: Work closely with platform engineers, data analysts and other stakeholders to understand data requirements, and document infrastructure, models and best practices.