Staff Backend Engineer: Take Profit Trader

Serv, a global executive recruitment partner, is hiring on behalf of our client, Take Profit Trader, for a Staff Backend Engineer. Join the Team: Take Profit Trader is a high-growth proprietary trading firm handling hundreds of millions of dollars in trading volume. Dedicated to innovation, operational excellence, and empowering traders globally, they are hiring a Staff Backend Engineer. This role is responsible for designing and owning the backend streaming infrastructure that processes live trading data and delivers low-latency alerts and signals to traders and internal systems. For more information, visit: Position Responsibilities: If you are someone who enjoys solving complex distributed systems challenges, building highly scalable backend infrastructure, and influencing technical direction across an engineering organization, this is an opportunity to own one of the most critical platforms within the business. This role includes: • Designing and building the real-time notification platform that delivers low-latency alerts and account updates to traders and internal systems • Building and operating Kafka/MSK streaming pipelines that ingest live trading data from multiple upstream systems • Designing scalable event-driven architectures with durable messaging, replay capabilities, and optimized partitioning strategies • Developing stateful stream processing applications that support risk detection, fraud monitoring, and market integrity initiatives • Designing and optimizing PostgreSQL and time-series database schemas for both operational workloads and historical analytics • Building and evolving production-grade .NET Core APIs that support trading, risk, and fraud systems • Defining service reliability standards, monitoring, alerting, and observability to ensure highly available systems • Driving architecture decisions across the engineering organization while mentoring senior engineers and contributing hands-on to implementation • Partnering closely with engineering leadership to establish technical standards and long-term platform strategy Qualifications • 10+ years of backend or distributed systems engineering experience • 3 to 4+ years operating at a Staff Engineer, Principal Engineer, or equivalent technical leadership level • Proven experience designing and operating high-throughput, low-latency, real-time production systems • Strong hands-on experience developing backend services and APIs using .NET Core • Deep expertise with WebSockets or comparable persistent connection technologies, including scaling, connection management, and delivery guarantees • Strong experience with Kafka, MSK, or similar streaming platforms, including topic design, partitioning, and consumer scaling • Experience building stateful stream processing applications using Kafka Streams, Flink, or similar technologies • Advanced SQL and PostgreSQL expertise, including replication strategies and schema optimization • Experience working with time-series databases such as TimescaleDB, ClickHouse, InfluxDB, or similar technologies • Strong understanding of distributed systems concepts including consistency models, idempotency, fault tolerance, delivery guarantees, and backpressure • Experience within trading, fintech, financial services, payments, fraud detection, or risk systems is highly preferred • Experience with AWS streaming services, infrastructure as code, or trade surveillance systems is a plus The perfect fit for our team is… A strategic operator: You think big but also execute at a high level A builder: You enjoy creating systems, teams, and processes from the ground up Data-driven: You test, measure, and iterate based on performance Highly organized: You manage multiple workstreams without losing detail A strong communicator: You move quickly and keep stakeholders aligned Proactive: You take initiative and identify opportunities without waiting for direction Creative and internet-native: You understand content trends, formats, and audience behavior Accountable: You take ownership of results and continuously look to improve The Hiring Process: We take our hiring process seriously and evaluate candidates based on both capability and culture fit. Our goal is to ensure you step into a role where you feel genuinely excited and fulfilled. That’s important for you, and it’s equally important for us. Below is a snapshot of what you can expect throughout our hiring process. Please note that steps may vary slightly depending on the position. Step One: Video interview with a recruiter Step Two: Video interview with your direct supervisor Step Three: Technical interview Four: Cultural interview Location: Fully Remote in the United States Compensation The expected annual salary range for this role is $160,000-$190,000 Actual compensation within this range will be determined by a number of factors, including but not limited to skills, experience, and qualifications, as well as any applicable employment laws. End-of-Year Discretionary Performance Bonus: up to 20% of annual salary, based on individual and company performance Benefits Medical, Dental and Vision insurance Health & Wellness stipend Unlimited PTO Company Holidays TakeProfitTrader provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. In compliance with federal law, all persons hired will be required to verify identity and eligibility to work in the United States and to complete the required employment eligibility verification form I9 upon hire. TakeProfitTrader participates in the E-Verify program Serv uses AI tools in our recruitment process, including developing job postings and note-taking during interviews. AI is not used to screen, assess, or select candidates. All resumes are reviewed by a human, and all hiring decisions are made by humans. Any AI-generated output used during the recruitment process is reviewed by a human before action is taken.