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At Wave, we help small businesses to thrive so the heart of our communities beats stronger. We work in an environment buzzing with creative energy and inspiration. No matter where you are or how you get the job done, you have what you need to be successful and connected. The mark of true success at Wave is the ability to be bold, learn quickly and share your knowledge generously. Reporting to the Manager, Data Engineering, as a Data Engineer you will be building tools and infrastructure to support the efforts of the Data Products and Insights & Innovation teams, and the business as a whole. We’re looking for a talented, curious self-starter who is driven to solve complex problems and can juggle multiple domains and stakeholders. This highly technical individual will collaborate with all levels of the Data & AI team as well as the various engineering teams to develop data solutions, scale our data infrastructure and advance Wave to the next stage in our transformation as a data-centric organization. This role is for someone with proven experience in complicated product environments. Strong communication skills are a must to bridge the gap between technical and non-technical audiences across a spectrum of data maturity.
Job Responsibility:
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:
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
At least 3 years of experience working with AWS cloud infrastructure, including Kafka (MSK), Spark / AWS Glue, and infrastructure as code (IaC) using Terraform
Fluency in SQL, strong understanding of data modelling principles and data storage structures for both OLTP and OLAP
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
Prior experience building data lakes on S3 using Apache Hudi with Parquet, Avro, JSON, and CSV file formats
Experience developing and deploying data pipeline solutions using CI/CD best practices to ensure reliability and scalability
Nice to have:
Experience developing or maintaining a production data system on Databricks is a significant plus
Familiarity with data governance practices, including data quality, lineage, and privacy, and experience using data cataloging tools to support discoverability and compliance
Working knowledge of tools such as Stitch and Segment CDP for integrating diverse data sources into a cohesive ecosystem
Experience with Athena, Redshift, or SageMaker Feature Store for analytics and ML workflows is a plus
What we offer:
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)