Senior Data Engineer

Company Overview: Zocks is an AI platform transforming how financial advisors work. Starting in the US financial services market, we're building technology that supports advisors across their entire workflow - from sales and meeting preparation to note-taking, follow-ups, scheduling, email, document intelligence, reporting, and integrations with the systems they use every day. Our goal is to take repetitive, manual work out of the advisor-client relationship and turn conversations, documents, and data into actions automatically. As our platform expands across more workflows, customers, and use cases, we're solving increasingly complex challenges around AI, real-time systems, data, security, integrations, and scale. Zocks is headquartered in San Francisco, while our entire R&D organization - Engineering, Product, Design, Data, and Infrastructure - is based in Budapest. This means our Budapest teams don't just execute on a product built elsewhere: they're directly responsible for shaping the product, technology, and how we build them as we grow. More than 5,000 firms, including 6 of the 9 Barron’s Top Mega RIAs and 2 of the top 3 life insurance carriers, rely on Zocks, the #1 rated AI Assistant for Financial Services on G2. Location: Budapest, XI., Science Park (On-site Monday to Thursday, Remote on Fridays) Why we're hiring for this role Data plays a central role in how we build and evolve our AI-native platform at Zocks, supporting our product capabilities, customer experiences, and business decisions. We're looking for a Senior Data Engineer to help develop and enhance our data infrastructure, ensuring reliability, scalability, and performance as our product and customer base continue to grow. You'll join our Data team and work on a mix of complex engineering challenges and new initiatives. From optimizing production data pipelines and advancing our data quality capabilities to contributing to new data lake solutions, you'll have the opportunity to make a meaningful impact on how data is processed, managed, and made available across Zocks. This is a hands-on engineering role with strong ownership. You'll design and implement technical solutions, contribute to architectural decisions, and work closely with other engineering teams to build reliable, scalable data systems. Job Description As a Senior Data Engineer at Zocks, you'll take ownership of developing, maintaining, and improving our data infrastructure, balancing product requirements with reliability, performance, and scalability. You'll work with technologies including ClickHouse, Dagster, Airbyte, Apache Superset, AWS, and Python, solving engineering challenges across our data ecosystem. Working closely with our Head of Data, Data Analysts, Platform Engineers, and Software Engineers building enterprise solutions, you'll translate data requirements into robust technical solutions. We're looking for someone who combines strong hands-on engineering skills with a proactive mindset, enjoys solving complex technical problems, and can independently deliver solutions from initial design through production. What You'll Own Build and evolve our data infrastructure Design, develop, and maintain production data pipelines, ensuring reliability, performance, scalability, and maintainability. Implement new data pipelines, integrations, and data processing capabilities to support our product. Contribute to the design and implementation of new data lake solutions. Optimize data processing, storage, and orchestration to support growing data volumes and complex use cases. Participate in architectural discussions, evaluate technical approaches, and propose improvements to existing solutions. Build reliable, production-grade data systems Develop and enhance automated data quality checks, monitoring, and alerting capabilities. Apply engineering best practices for data pipeline testing, observability, documentation, and reliability. Identify, troubleshoot, and resolve data quality issues, pipeline failures, and performance bottlenecks. Improve data consistency, completeness, and availability across systems. Take end-to-end ownership of assigned data engineering solutions, from implementation to production operation. Collaborate across Data and Engineering Work closely with the Head of Data and Data Analysts to understand data requirements and deliver reliable, accessible data for analytics and reporting. Collaborate with Platform Engineering and DevOps on cloud infrastructure, deployment, monitoring, and operational reliability. Partner with Software Engineering teams to integrate new data sources and support enterprise product requirements. Contribute to technical discussions, share knowledge, and help identify opportunities to improve our data engineering practices. Proactively suggest solutions, evaluate technical trade-offs, and collaborate with other engineers on implementation decisions. Required Skills and Qualifications 5+ years of hands-on experience in Data Engineering or closely related software engineering disciplines, including experience working with production data systems. Strong Python and SQL skills, with experience designing, developing, and maintaining reliable data pipelines. Solid understanding of ETL/ELT, data modeling, pipeline orchestration, and modern data architectures. Hands-on experience with cloud-based data infrastructure, preferably AWS. Experience working with data warehousing, data storage, and data processing technologies. Experience with data quality, monitoring, testing, troubleshooting, and performance optimization. Ability to independently design and implement technical solutions, evaluate trade-offs, and solve complex engineering problems. Experience collaborating with Data, Infrastructure, and Software Engineering teams. Strong problem-solving skills, a proactive mindset, and a sense of ownership. Excellent English communication skills; fluent Hungarian is also required. Preferred Qualifications Hands-on experience with our technology stack: ClickHouse, Dagster, Airbyte, and Apache Superset. Experience working with data lakes or lakehouse architectures. Experience implementing automated data quality checks, monitoring, and alerting. Familiarity with Infrastructure as Code, CI/CD, and modern DevOps practices. Experience working in a B2B SaaS or data-intensive product environment. Exposure to AI-native products or data infrastructure supporting AI/ML use cases.