Data Engineer

Koa Health is a fast-growing digital technology company focused on delivering innovative digital mental health solutions to healthcare providers. We are revolutionising mental health care by delivering therapeutic solutions through smartphones. Our mission is to make mental health support accessible, personalised, and effective by leveraging cutting-edge technology and data. We have made it our goal to combine the best research and technology to help people improve their mental health using a digital-first approach. Our market-leading evidence base shows that we have succeeded.

Having recently, successfully, secured Series B funding and as the demand for our services continues to grow, so must our teams. We are therefore now seeking an accomplished Data Engineer to join our Data Team and embark on this exciting and critical moment in Koa Health’s journey.

Your new role:

As a Data Engineer within the Data Team at Koa, your main focuses and responsibilities will be the following ones: 

• Data Products Development: Build and maintain scalable products, such as APIs accessing data from the datalake, and reporting tools for self-service analytics for external customers. Rethink and contribute to the definition and development of a scalable data architecture supporting the iteration of such data products.
• Code Quality: Maintain coding standards and best practices, prioritising clean, production-ready code, and treating data pipelines as a final product for internal and external customers. Apply modular design principles, creating components that can be adapted to new data sources or new transformations. (python, scala, SQL).
• Data Ingestion and APIs: Build and maintain ingestion pipelines in our AWS datalake, integrating new data sources, both SQL and noSQL, pulling data via APIs for integration with various systems, such as NetSuite, Hubspot, and other external clinical partners.
• New Tools exploration: Investigate and implement tools, with special focus on the AWS suite (like Athena, Lake Formation, Glue, DMS, AppFlow), to enhance platform scalability, accessibility, and compliance with strict privacy regulation.
• Data Pipelines framework: contribute to our work-in-progress framework to develop scalable, reliable, and maintainable data pipelines, using tools such AWS Glue, Step Functions, and Airflow. Advocate for data pipeline best practices, including idempotency:  to handle reprocessing of historical data efficiently, ensuring data integrity (no duplicate effect, and reproducibility).
• Self-service analytics: Optimize and extend the use of our recently introduced BI tool, Superset, ensuring seamless integration with our data stack, to deliver scalable self-service reporting solutions for customers. Ensure the BI platform aligns with Koa data governance and security standards, while enabling flexibility for ad-hoc reporting and analysis.
• Custom datasets and Dashboards: Create tailored datasets to support product and other business functions. Develop and maintain automated reports, analyses, and dashboards to monitor the performance of key product features, ensuring that stakeholders have access to the most updated and reliable insights.

As a secondary responsibility, you may also contribute to the following ongoing activities and streamlines:

• Data Quality frameworks: Improve data quality by improving schema validation during data ingestion. Detects and flag issues such as missing fields, incorrect types, or schema mismatches at the source. Develop automated tests and quality checks integrated into pipelines, covering data completeness and consistency, both across ingestion and processing layers.
• Observability frameworks: Integrate monitoring tools such as Amazon CloudWatch, Datadog, and Spark UI logs to provide comprehensive visibility into the platform's health. Aggregate logs from various sources (e.g., Spark application logs, CloudWatch logs) into centralised systems like Datadog for unified analysis, and create dashboards and alerting rules for critical events, such as job failures, task delays, resource bottlenecks, and pipeline errors.
• Stakeholder Support: Provide support to Sales, Marketing, Clinical teams, and other key stakeholders, by delivering internal data product and business KPIs. This includes the development of automated reporting systems to ensure seamless access to critical metrics.
• Collaborate with Product Teams: Work closely with product teams, ideally as an embedded member, to ensure a data-driven approach is integrated into every aspect of product development and strategy.
• Define Analytics Tracking: Lead the definition and implementation of analytics tracking for in-app events, particularly when new features are developed, new pages are added, or new clients are onboarded.
• ML/AI Use Case Development: Collaborate closely with the R&D team to identify, design, and develop machine learning and AI use cases. 

You will have:

• Programming Skills: Highly proficient in Python, specially PySpark (for data engineering and analytics jobs), SQL (advanced querying, and optimization), Nice-to-have: Scala.
• Data Engineering experience: 3+ years of experience in Data pipelines development (e.g. AWS Glue, Airflow, DBT or similar). Ideally, working with event-driven architectures (Kinesis, Kafka).
• AWS Expertise: 3+ years of experience working with at least some of the AWS services for data development (e.g. S3, Athena, Lake Formation, Glue, Step Functions, EMR, AppFlow, DMS, etc.).

Nice-to-have:

• Business Intelligence & Visualisation: Experience building dashboards with a BI tool (such as Superset, Tableau, Metabase). Nice-to-have: familiarity with visualisation libraries, such as Plotly.
• Analytics engineering experience: Experience of creating modular, version-controlled, and testable pipelines that are usable both for data experts and for final stakeholders (e.g. datamarts, datawarehouse, reporting layers…)
• Product Analytics: Experience with in-app analytics tracking, A/B testing methodologies and frameworks to assess the impact of product changes.

Soft Skills:

• Product-driven: you embrace an iterative approach to product development, focusing on continuous trade-offs between tailored solutions and scalable architectures that align with evolving  business needs.
• Communication: You are able to collaborate across both business teams (product, sales, marketing, customer success) and engineer teams (DevOps, Cyber, Pods engineers), explaining technical concepts to non-technical stakeholders, and filling the gap between these two groups.
• Growth mindset: You love debugging data pipeline issues, advocating for data quality, as much as for continuously improving code quality. You see a bug or an error as an opportunity to learn and improve our processes, pipelines and products.
• Team Player: Strong collaborator, open to learning beyond their comfort zone, and dedicated to mentoring team members to enhance coding skills and best practices.