Senior Data Engineer

Remote - GlobalRemotefull-timesenior

Data EngineerData

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Quick Summary

Key Responsibilities

Architect and maintain robust, scalable and secure data infrastructure on Databricks, covering both streaming and batch workloads. Data Pipeline Development: Design,

Requirements Summary

3+ years as a Data Engineer, with hands-on ownership of production pipelines and lakehouse or data warehouse architecture. Databricks and Spark: Strong practical experience with Databricks,

Technical Tools

Data EngineerData

Trust Wallet is the leading non-custodial cryptocurrency wallet, trusted by over 200 million people worldwide to securely manage and grow their digital assets. Our vision is to give individuals the freedom to own their assets, confidently participate in the future economy, and access opportunities that enhance their lives. Our mission is to be a trusted personal companion — helping users safely navigate Web3, the on-chain economy, and the emerging AI-powered future. With support for over 10 million assets across 100+ blockchains, Trust Wallet offers a seamless, multi-chain experience backed by industry-leading self-custody technology, a vibrant community, and a growing ecosystem of partners.

About the Role

~1 min read

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.

Responsibilities

~1 min read

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Data Platform Engineering: Architect and maintain robust, scalable and secure data infrastructure on Databricks, covering both streaming and batch workloads.
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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.
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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.
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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.
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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.
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Performance, Scalability and Cost: Optimise pipelines and storage for performance, reliability and cost efficiency at scale.
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Data Governance and Security: Apply best practices for governance, security and compliance in cloud and Databricks environments, including access control, encryption and monitoring.
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Collaboration and Documentation: Work closely with platform engineers, data analysts and other stakeholders to understand data requirements, and document infrastructure, models and best practices.

Requirements

~1 min read

Experience in Data Engineering: 3+ years as a Data Engineer, with hands-on ownership of production pipelines and lakehouse or data warehouse architecture.
Databricks and Spark: Strong practical experience with Databricks, Delta Lake and Spark, including both streaming and batch processing, and the judgement to choose between them.
Data Modelling: Solid understanding of dimensional modelling, slowly changing dimensions and data warehouse design, with the ability to define and defend the grain of the models you build.
Transformation Frameworks: Experience with dbt or an equivalent framework for managing transformations, testing and lineage.
Cloud Proficiency: Strong cloud fundamentals, including identity and access management, object storage, networking and infrastructure as code. We run on AWS; deep experience with Azure or GCP transfers well.
Proficiency in Python and SQL: Comfortable writing production transformations as well as services and connectors.
Data Quality and Governance: Experience implementing testing, monitoring and governance practices in cloud data environments.