Staff Data Engineer

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.

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 Staff Data Engineer to take a leading technical role in developing 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 designing and implementing new data lake solutions, you'll have the opportunity to shape the technical direction of data engineering at Zocks.

This is a hands-on individual contributor role with significant technical ownership. You'll drive architectural decisions, champion engineering best practices, and build solutions that scale with our product and organization.

Job Description

As a Staff Data Engineer at Zocks, you'll drive the technical design, development, and evolution of our data infrastructure, balancing immediate product needs with long-term reliability, performance, and scalability.

You'll work with technologies including ClickHouse, Dagster, Airbyte, Apache Superset, AWS, and Python, taking ownership of complex 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 deep hands-on engineering expertise with strong architectural thinking, challenges existing approaches, and can drive complex technical initiatives from initial design through production.

What You'll Own

Shape and evolve our data architecture

• Drive technical decisions around data architecture, pipeline design, orchestration, storage, and processing.
• Own the evolution of production data pipelines, improving their reliability, performance, scalability, and maintainability.
• Play a key technical role in designing and implementing new data lake solutions, including architectural choices and implementation.
• Design and implement new data pipelines, integrations, and data processing capabilities to support our product.
• Evaluate technologies and architectural approaches, making pragmatic trade-offs between short-term delivery and long-term scalability.

Build reliable, production-grade data systems

• Develop and advance automated data quality checks, monitoring, and alerting capabilities.
• Define and champion engineering best practices for data pipeline testing, observability, documentation, and reliability.
• Proactively identify and address performance bottlenecks, data inconsistencies, and architectural limitations.
• Improve data processing and storage efficiency to support growing data volumes and complex use cases.
• Take end-to-end ownership of critical data engineering solutions, from design and implementation to production operation.

Drive technical collaboration and engineering standards

• Partner with the Head of Data to shape technical priorities and the long-term evolution of our data engineering capabilities.
• Work closely with Data Analysts to understand data requirements and ensure 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.
• Lead technical discussions, challenge assumptions, and help teams make well-informed architectural decisions.
• Share technical knowledge, promote reusable patterns, and champion engineering standards across the Data team.

Required Skills and Qualifications

• 7+ years of hands-on experience in Data Engineering or closely related software engineering disciplines, including experience owning complex production data systems.
• Expert-level Python and SQL skills, with a strong track record of designing and building reliable, scalable data pipelines.
• Deep understanding of ETL/ELT, data modeling, pipeline orchestration, and modern data architectures.
• Strong hands-on experience with cloud-based data infrastructure, preferably AWS.
• Proven experience making architectural decisions and leading complex technical initiatives from design through production.
• Solid understanding of data warehousing, data storage, distributed data processing, and performance optimization.
• Experience with engineering practices around data quality, monitoring, testing, and operational reliability.
• Ability to independently identify technical challenges, evaluate trade-offs, and drive solutions without relying on predefined requirements.
• Experience collaborating across Data, Infrastructure, and Software Engineering teams, with the ability to influence technical decisions beyond your immediate area.
• 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 designing and implementing data lakes or lakehouse architectures.
• Experience building automated data quality frameworks, validation processes, and alerting systems.
• Familiarity with Infrastructure as Code, CI/CD, and modern DevOps practices.
• Experience shaping data engineering standards and infrastructure in a product organization.
• Background in B2B SaaS or data-intensive product development.
• Exposure to AI-native products or data infrastructure supporting AI/ML use cases.
• Experience mentoring engineers or providing technical guidance across teams.