Senior Data Engineer

The current delivery model for mental health care is broken in this country. PsychPlus set out on a mission to reimagine how mental health care is delivered to folks who need it. Our goal is to provide a digital-first, modern approach to psychiatry and therapy. We provide care both in-person at our offices across Texas and virtually so our patients can be seen at their convenience.

Through a combination of exceptional medical and psychological care and best-in-class technology, we provide an unparalleled approach that serves our patients' needs in an integrated way. Join us in our mission to ensure that every person has access to affordable and accessible mental health care.

About The Role

We are seeking a dedicated and experienced Senior Data Analytics Engineer to join our healthcare-focused technology team. As a Senior Data Engineer in the healthcare industry, you will be an integral part of developing cutting-edge data products that contribute to improving patient care, streamlining healthcare operations, by applying machine learning / artificial intelligence capabilities, while ensuring data security and compliance.

Responsibilities

• Custom datasets and Analytics: Create tailored datasets to support Product, Operations, Clinical Teams, and other business functions.
• Data Pipelines framework: Build new and maintain existing ingestion pipelines in AWS datalake,. Contribute to improving our data pipelines framework to develop scalable, reliable, and maintainable data pipelines, by using AWS services such as Glue, EMR, and Step Functions. Advocate for data pipeline best practices, including idempotency.
• Data Quality frameworks: Detect 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 data consistency, both across ingestion and processing layers.
• Stakeholder Support: Provide support to Sales and Clinical teams, by delivering internal data product and business KPIs. This includes the development of automated reporting systems to ensure seamless access to critical metrics. 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.

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

• ML & AI pipelines: Contribute to building machine learning pipelines in Python, collaborating closely with AI Scientists and AI Engineers.
• Define Analytics Tracking: Lead the definition and implementation of analytics tracking for in-app events, particularly when new features are developed, such as new pages and buttons.

Requirements

• Data Engineering experience: 6+ years of experience as a Data Engineer, creating modular, version-controlled, and testable pipelines, with 2+ years working with AWS services for data pipelines. Nice-to-have: experience with Microsoft Fabric.
• Data modelling experience: solid understanding of data modelling and best practices, and some experience implementing data modelling in silver layer from scratch.
• Programming Skills: Highly proficient in Python, specially PySpark, SQL (advanced querying, and optimization). Nice-to-have: Scala.
• Experience implementing data quality and schema validation frameworks at ingestion time (e.g., data contracts, schema enforcement, automated validation checks).
• (Nice-to-have) Product Analytics: Experience with in-app analytics tracking, A/B testing methodologies and frameworks to assess the impact of product changes.
• (Nice-to-have) Experience working in environment with strict data security requirements, ideally under HIPPA compliance and/or GDPR.

Soft Skills

• Communication: You can communicate clearly about technical decisions, considering pros and cons, and making them clear to your audience. You are also a good listener and can make questions to understand the context.
• 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.