Data Engineer at Motive delivering data infrastructure for the AI era. Collaborating with stakeholders, building models, and implementing innovative tooling.
About the role
- Data Engineer at Wave, building tools and infrastructure for data products and insights to support small businesses. Collaborating with teams for data-centric organizational transformation.
Responsibilities
- Design, build, and deploy components of a modern data platform, including CDC-based ingestion using Debezium and Kafka, a centralized Hudi-based data lake, and a mix of batch, incremental, and streaming data pipelines.
- Maintain and enhance the Amazon Redshift warehouse and legacy Python ELT pipelines, while driving the transition to a Databricks and dbt–based analytics environment that will replace the current stack.
- Build fault-tolerant, scalable, and cost-efficient data systems, and continuously improve observability, performance, and reliability across both legacy and modern platforms.
- Partner with cross-functional teams to design and deliver data infrastructure and pipelines that support analytics, machine learning, and GenAI use cases, ensuring timely and accurate data delivery.
- Work autonomously to identify and implement opportunities to optimize data pipelines and improve workflows under tight timelines and evolving requirements.
- Respond to PagerDuty alerts, troubleshoot incidents, and proactively implement monitoring and alerting to minimize incidents and maintain high availability.
- Provide technical guidance to colleagues, clearly communicating complex concepts and actively listening to build trust and resolve issues efficiently.
- Assess existing systems, improve data accessibility, and deliver practical solutions that enable internal teams to generate actionable insights and enhance the experience of our external customers.
Requirements
- Data Engineering Expertise: 3+ years of experience building data pipelines and managing a secure, modern data stack, including CDC streaming ingestion (e.g., Debezium) into data warehouses that support AI/ML workloads.
- AWS Cloud Proficiency: At least 3 years of experience working with AWS cloud infrastructure, including Kafka (MSK), Spark / AWS Glue, and infrastructure as code (IaC) using Terraform.
- Data modelling and SQL: Fluency in SQL, strong understanding of data modelling principles and data storage structures for both OLTP and OLAP.
- Databricks experience: Experience developing or maintaining a production data system on Databricks is a significant plus.
- Strong Coding Skills: Experience writing and reviewing high-quality, maintainable code to improve the reliability and scalability of data platforms, using Python, SQL, and dbt, and leveraging third-party frameworks as needed.
- Data Lake Development: Prior experience building data lakes on S3 using Apache Hudi with Parquet, Avro, JSON, and CSV file formats.
- CI/CD Best Practices: Experience developing and deploying data pipeline solutions using CI/CD best practices to ensure reliability and scalability.
- **Bonus points for:**
- Data Governance Knowledge: Familiarity with data governance practices, including data quality, lineage, and privacy, and experience using data cataloging tools to support discoverability and compliance.
- Data Integration Tools: Working knowledge of tools such as Stitch and Segment CDP for integrating diverse data sources into a cohesive ecosystem.
- Analytical and ML Tools Expertise: Experience with Athena, Redshift, or SageMaker Feature Store for analytics and ML workflows is a plus.
Benefits
- Bonus Structure
- Employer-paid Benefits Plan
- Health & Wellness Flex Account
- Professional Development Account
- Wellness Days
- Holiday Shutdown
- Wave Days (extra vacation days in the summer)
- Get A-Wave Program (work from anywhere in the world up to 90 days)
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