[Hiring] Data Engineer @Weekday AI

Role Description

As a Data Engineer, you will design, build, and maintain scalable data pipelines and analytics platforms that drive business insights and product innovations. Leveraging your expertise in Python and SQL, you will transform raw data into clean, structured datasets, enabling data scientists and product teams to accelerate experimentation. You will integrate Posthog for behavioral analytics, employ deepeval for model validation workflows, and use Streamlit and LangChain to create interactive data tools and prompt-based applications. Your role will ensure data quality, performance, and compliance across the organization while collaborating closely with cross-functional stakeholders to translate data needs into technical solutions.

Qualifications

• 3+ years of professional experience building data pipelines and analytics platforms in a production environment.
• Strong programming skills in Python, with extensive experience in writing reusable, testable code.
• Deep knowledge of SQL and experience with modern database systems such as PostgreSQL, BigQuery, or Snowflake.
• Proven track record of integrating Posthog for behavioral data ingestion and analysis.
• Hands‑on experience with deepeval or similar model evaluation frameworks.
• Familiarity with LangChain for building LLM‑driven data workflows and prompt engineering.
• Solid understanding of Streamlit or comparable open‑source toolkits for building interactive dashboards.
• Ability to collaborate effectively with cross‑functional teams in a remote, agile environment.
• Strong problem‑solving mindset, with the capacity to troubleshoot complex data issues under tight deadlines.
• Commitment to code quality, documentation, and continuous learning in the fast‑moving data engineering landscape.

Requirements

• Design and implement robust ETL pipelines that ingest, transform, and store data from diverse sources into a unified data warehouse.
• Build and maintain dashboards and data visualizations using Streamlit to provide real‑time insights for product, marketing, and engineering teams.
• Integrate Posthog event data into the analytics pipeline and create segmented user funnels for product optimization.
• Develop and maintain LangChain workflows to power conversational AI features that interact with structured data.
• Employ deepeval to automate evaluation of machine learning models, ensuring model performance aligns with business KPIs.
• Optimize SQL queries and database schemas for speed, reliability, and scalability across large datasets.
• Collaborate with data scientists to provision and manage queryable data lakes and data marts.
• Implement data governance, security, and privacy controls to comply with industry regulations.
• Monitor pipeline health, troubleshoot failures, and iterate on performance enhancements.
• Document data architecture, pipeline logic, and best practices for internal and external stakeholders.

Company Description