Accountabilities

• Design, build, and maintain scalable and reliable data pipelines using Python, PySpark, and SQL.
• Develop, optimize, and maintain ETL and data transformation processes to deliver accurate and relevant information for internal reporting and products.
• Collaborate with Product Managers, Designers, Leadership, and Engineering teams to understand requirements and translate them into effective data solutions.
• Build scalable, well-structured, and discoverable data models using SQL, supported by clear and comprehensive documentation.
• Implement data quality checks, monitoring, and validation processes to maintain data accuracy, consistency, and integrity.
• Optimize data storage and retrieval processes for performance, scalability, and cost efficiency.
• Contribute to the optimization and maintenance of data-processing clusters and ensure efficient resource utilization.
• Establish and promote data engineering best practices across processing, modeling, documentation, and development workflows.
• Document technical processes, data models, and engineering practices clearly for internal stakeholders and, where appropriate, client-facing audiences.
• Continuously improve team workflows, engineering processes, and development practices.
• Take ownership of individual objectives and contribute to broader team and business goals through measurable outcomes.
• Proactively investigate data challenges, conduct research, make informed technical decisions, and drive solutions independently.
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Requirements

• 2–4 years of professional experience in a Data Engineering role, ideally involving internal, financial, or operational reporting.
• Strong proficiency in Python, including dataframes, object-oriented programming, modularity, and maintainable code practices.
• Practical experience with PySpark and large-scale data processing.
• Strong SQL skills, including the ability to write complex queries, data transformations, and data quality checks.
• Proven experience with data modeling and query optimization.
• Experience building or maintaining scalable data infrastructure in a growing SaaS or technology environment is highly desirable.
• Experience with Databricks and dbt is a strong advantage.
• Familiarity with databases such as ClickHouse is a plus.
• Understanding of DevOps principles and CI/CD practices is advantageous.
• AI fluency and familiarity with emerging AI development concepts such as MCP, agents, and cross-agent review is a plus.
• Strong analytical and problem-solving abilities, with creativity, independent thinking, and a proactive approach to technical challenges.
• Ability to take ownership, conduct independent research, and make sound conclusions in an evolving environment.
• Strong collaboration and communication skills, with the ability to work effectively with engineers, product teams, designers, leadership, and other stakeholders.
• Strong organizational and time-management skills, including the ability to prioritize work independently and manage tight deadlines.
• Comfortable working under pressure while maintaining high standards of quality.
• Full professional proficiency in English, both written and spoken.
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