Software Engineer, Data Systems

About the role

The team balances hands-on implementation with technical leadership across analytics, product, and engineering. You will own the design and execution of data infrastructure that powers critical product and analytics workflows, translating ambiguous requirements into robust, scalable solutions. You will act as a technical leader for data systems, guiding decisions on architecture, trade-offs, and implementation details with clarity and conviction. A core part of this role involves owning end-to-end delivery of data pipelines, ensuring reliability, performance, and maintainability from ingestion through consumption. You will collaborate closely with analysts, product managers, and other engineers to align data platform work with business objectives and user needs. This position requires you to communicate technical concepts effectively to both technical and non-technical stakeholders, ensuring shared understanding and alignment. You will mentor and elevate the engineering practices of peers by setting standards for code quality, testing, and operational excellence.

Key facts

What you'll do

Design and maintain distributed data systems that reliably carry event streams from Kafka ingestion to analytical stores like Snowflake, ensuring high availability and fault tolerance.

Provide technical direction for data infrastructure, defining standards, patterns, and best practices that enable scalable and maintainable solutions.

Architect and implement CDC pipelines and streaming transformations that process massive volumes of data with low latency and high throughput.

Optimize Snowflake performance, cost, and scalability through thoughtful modeling, partitioning strategies, and query optimization techniques.

Lead major data infrastructure initiatives that support orders-of-magnitude growth, balancing immediate needs with long-term platform evolution.

Establish and enforce best practices for data engineering across the organization, including monitoring, alerting, and operational runbooks.

Collaborate with analytics and product teams to ensure data systems meet analytical, reporting, and product requirements with accuracy and timeliness.

Partner with software engineers to build and maintain integrations between data platforms and application services, focusing on resilience and maintainability.

Drive technical strategy for data infrastructure by evaluating new tools, frameworks, and cloud capabilities in the context of business requirements.

Promote data governance, privacy compliance, and security best practices across pipelines, schemas, and access controls.

Work with dbt and Terraform to build version-controlled, testable, and repeatable data pipelines and infrastructure as code.

Analyze and resolve complex data pipeline issues by correlating logs, metrics, and traces to minimize downtime and data inconsistencies.

Mentor engineers and analysts on effective data modeling, SQL design, and performance tuning for analytical workloads.

Contribute to on-call responsibilities and incident response, ensuring rapid diagnosis and remediation of production issues.

Requirements

You bring 10+ years as a data or software engineer with deep expertise in distributed systems, data infrastructure, and high-growth SaaS products at massive scale.

You possess expert-level knowledge of Apache Kafka producers, consumers, Kafka Connect, and stream processing frameworks for real-time data pipelines.

You have extensive hands-on experience with Snowflake performance optimization, cost management, and data modeling for complex analytical workloads.

You maintain a strong foundation in Postgres, CDC patterns, and replication strategies, including change data capture and schema evolution.

You have architected and led major data infrastructure initiatives that scaled through orders-of-magnitude growth in data and user load.

You have experience establishing best practices and driving technical strategy across engineering and analytics organizations.

You communicate strongly to influence technical direction across engineering, analytics, and leadership, translating complexity into clear decisions.

You demonstrate proficiency with dbt and Terraform, using them to build reliable, observable, and version-controlled data infrastructure.

You have working knowledge of data governance, privacy compliance (GDPR, CCPA), and security best practices in data platforms.

You are comfortable operating in ambiguous environments, making sound technical decisions with incomplete information.

You take ownership of outcomes, driving projects from conception through production and post-mortem analysis.

You write clean, testable, and maintainable code, and you expect the same from your peers through thoughtful code reviews.

You understand the fundamentals of networking, operating systems, and storage systems as they relate to data infrastructure.

You have a track record of mentoring engineers and contributing to technical standards and processes.

Practical notes

Typical interview steps

Hiring for engineering roles usually starts with a recruiter screen, followed by one or two technical rounds. Candidates often solve a coding problem, discuss past projects, and answer system design questions. Some loops include a take-home task. Final rounds typically cover team fit and give candidates a chance to ask questions. Interviewers look for how you break down an unfamiliar problem, not just whether you reach the answer. Practicing a few problems aloud and reviewing your own past projects are the best preparation.

Good to know

Data engineers design and operate large-scale data platforms. Common tools include stream processing frameworks, SQL engines, workflow orchestrators, and version controlled infrastructure code. Roles in this domain often balance deep implementation work with cross-team collaboration and strategic decisions.

Questions to ask

Worth asking in any interview: how the team measures success, who the role works with daily, what the onboarding looks like, and what the company is trying to achieve this year. Asking what past hires did well is a strong final question. Keep the list short and pick the questions that matter most to you.

Career growth

Engineering careers usually progress from individual contributor to senior, staff, and principal levels. Some engineers move into management and lead teams of five to twenty people. Others stay on the technical track. Growth follows demonstrated impact, not tenure alone. A typical engineering ladder has clear levels with defined expectations for scope, quality, and mentorship. Moving up usually requires owning outcomes end to end rather than completing assigned tickets.

About the company

Gamma is an AI-powered presentation and document creation platform. The tool enables users to create professional presentations, documents, and webpages using AI-generated content and design.