Build and scale data systems that drive WorkWave's "decision intelligence" products, empowering customer-facing data products through AI/ML enablement.
Requirements
- 5+ years of experience in data engineering, with 1–2+ years in a senior or lead capacity.
- Profound understanding of ML models and can articulate the trade-offs between different architectures (e.g., complexity vs. inference speed, accuracy vs. interpretability) to ensure the right tool is selected for the job.
- "Ninja-level" proficiency in Python for complex data structures and automation, alongside strong SQL expertise.
- Strong familiarity with Scikit-Learn and similar libraries, with specific experience building and maintaining associated feature engineering pipelines.
- High proficiency in MLOps practices and orchestration tools (e.g., Airflow, dbt, Dagster) to manage model lifecycles and data dependencies.
- Solid experience with modern data platforms (e.g., Snowflake, BigQuery, Redshift, or Databricks).
- Strong understanding of data modeling, performance optimization, and cloud computing (AWS, GCP, or Azure).
Responsibilities
- Build trusted AI/ML predictions and forecasts via feature pipelines, model input/output data flows, and robust data validation frameworks.
- Design and implement reliable, scalable, and secure data pipelines that serve analytical and product use cases.
- Provide technical leadership and mentorship to other data engineers and cross-functional collaborators, fostering a culture of engineering excellence.
- Own the architecture and evolution of our data platform, ensuring it meets the performance, scalability, and agility needs of our growing business.
- Implement data governance, quality, and observability best practices to ensure trustworthy insights, proactively managing data health before it impacts the business.
- Optimize cloud data infrastructure for cost, performance, and maintainability, treating the platform as a product rather than just a utility.
- Collaborate closely with product managers, engineers, and customer stakeholders to understand context and needs, and help translate them into engineering solutions.
Other
- Customer-centric
- Strategic mindset
- Strong communication skills
- High sense of ownership
- Excellent communication and collaboration skills, with a proven ability to work effectively across technical and non-technical teams.
