Data Platform Engineer

At Treatwell, we look for passionate team players who thrive in dynamic environments. As a Data Platform Engineer, you'll need experience with data integration tools like Airbyte and hands-on expertise in Python, Terraform, and AWS. Communication skills are crucial since you will collaborate with both technical and non-technical stakeholders to ensure our data solutions are effective and aligned with business objectives.

The Data Platform Engineer role at Treatwell is pivotal in driving our mission to digitally revolutionize the hair and beauty community. By building scalable and secure data solutions, you ensure that our analysts and product managers can make informed, data-driven decisions that directly enhance our services for over 50,000 salon partners across Europe.

In your role as a Data Platform Engineer at Treatwell, you will work extensively with technologies like Python and Terraform. Our tech stack also includes various open-source platforms such as Airbyte, DBT Core, and Grafana, alongside cloud technologies utilizing Amazon AWS components like Redshift and S3. Familiarity with CI/CD processes and observability frameworks will also be advantageous.

Absolutely! Treatwell offers a 100% remote position for the Data Platform Engineer role, allowing you to work from anywhere while maintaining flexible working hours. We believe in empowering our team members to work in an environment where they can excel!

Treatwell places a strong emphasis on personal and professional growth. As a Data Platform Engineer, you will have access to growth funds to further your training and development. We also encourage knowledge sharing and collaboration across teams to foster an environment of continuous learning.

At Treatwell, we pride ourselves on having a vibrant and inclusive team culture. Data Platform Engineers work in agile, high-performance teams that encourage collaboration, creativity, and innovation. We celebrate diverse perspectives and ensure that every voice is heard as we work towards our shared goals.

A typical day for a Data Platform Engineer at Treatwell involves collaborating with teammates to enhance our data platform, managing our Redshift Data Warehouse, and ensuring the robustness of our data pipelines. You’ll spend time analyzing system performance and implementing improvements while also communicating with analytics engineers to tackle data integration challenges.

In my previous roles, I have used Python extensively to build scalable data tools, utilizing libraries such as Pandas and NumPy for data manipulation and analysis. I adhere to best practices in coding and testing, ensuring that my code is not only effective but also maintainable and understandable by team members.

Securing a data platform is critical. I focus on implementing strict access controls, regularly performing security audits, and complying with best practices to protect sensitive data. In addition, I stay updated on security trends and incorporate them into my infrastructure, such as using AWS Secrets Manager for secure credential management.

I have hands-on experience with Airbyte, using it to integrate various data sources seamlessly into our data warehouse. My focus was on ensuring that the pipeline runs efficiently and is fault-tolerant. Additionally, I implemented monitoring mechanisms to immediately alert us to any potential issues.

In a past project, we faced frequent pipeline failures due to environment inconsistencies. To solve this, I created Docker containers to standardize our development and production environments. This not only reduced errors but also streamlined the deployment process, resulting in a 30% decrease in failure rates.

I begin by analyzing performance metrics and identifying bottlenecks. Based on this data, I implement targeted optimizations, such as indexing strategies in our data warehouse or fine-tuning ETL processes. Additionally, I continuously measure the effects of these changes to ensure we're on the right track.

In a previous role, I collaborated closely with product managers to define KPIs for our data reporting. Through regular meetings, we identified data needs and I developed streamlined data pipelines to deliver insights in real-time. This collaboration not only improved our reporting speed but also enhanced decision-making across teams.

Documentation is a cornerstone of effective collaboration and knowledge transfer. I prioritize creating clear and comprehensive technical documentation for processes and architecture to ensure team members can access information easily. This approach minimizes disruptions and enhances our ability to onboard new team members efficiently.

I regularly read tech blogs, participate in online forums, and attend webinars to stay informed about industry trends. Additionally, I actively explore new tools and technologies in side projects, allowing me to evaluate their potential impact on our existing systems.

Yes, I had an instance where a data pipeline was failing intermittently. I methodically traced back the logs to identify where the failure occurred, analyzed the data flow, and discovered a data format inconsistency from one of our sources. I implemented a validation step to catch this issue early in the pipeline.

I utilize several techniques to measure the effectiveness of data pipelines, including monitoring success rates, latency, and data integrity checks. By capturing metrics through observability tools like Grafana, I can assess performance trends and make informed adjustments to our processes.