Posted at: 21 July
Software Engineer
Company
Traackr is a global B2B SaaS platform headquartered in multiple locations including San Francisco and Paris, specializing in data-driven influencer marketing solutions for brands across various industries.
Remote Hiring Policy:
Traackr is a fully remote company hiring globally, with team members located in various regions including North America, Europe, and Asia-Pacific. We support remote collaboration across time zones (UTC-8 to UTC+10).
Job Type
Full-time
Allowed Applicant Locations
Worldwide
Salary
$90,000 to $120,000 per year
Job Description
Responsibilities:
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Own backend features end-to-end: discovery, design, implementation, rollout, and ongoing reliability and operations, with support from more experienced teammates as needed.
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Help design and evolve distributed systems (services, pipelines, and data stores) with an eye toward performance, scalability, and resiliency.
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Build and maintain APIs and data access patterns that support analytics and search use cases.
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Develop, maintain, and optimize scalable data pipelines that power product features, analytics, and machine learning workloads.
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Ensure data reliability, quality, and performance across our systems, and monitor and troubleshoot pipelines to ensure consistent, timely delivery.
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Build strong engineering habits: thoughtful code reviews, solid testing, incident readiness, and operational excellence.
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Apply an experimentation-first approach: define hypotheses and success metrics/guardrails, run controlled rollouts and A/B tests when appropriate, and write clear readouts for stakeholders.
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Use AI coding tools like Claude Code productively and responsibly as part of your development workflow - for implementation, debugging, refactoring, and design reviews - while maintaining high standards for correctness, security, and privacy.
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Bring evaluation discipline to AI-assisted work: treat prompts and configs like versioned artifacts, design regression tests, measure quality changes, and monitor for drift the same way you would for performance or correctness.
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Grow continuously: actively seek feedback, learn new tools, languages, and domains quickly, and apply what you learn to your work.
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Collaborate with Product Managers and fellow Engineers to ship intelligent, data-driven products.
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Share knowledge with teammates through clear documentation, pairing, and participation in code reviews.
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Document systems, pipelines, and architecture, and help evolve our engineering best practices.
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Stay current with emerging tools, frameworks, and trends across software, data, and AI engineering.
Requirements:
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2-4 years of professional software engineering experience building backend systems, and a desire to grow into larger distributed systems challenges.
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A growth mindset: curiosity, a habit of learning new tools and domains quickly, and openness to feedback.
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Strong general-purpose programming skills and software engineering fundamentals.
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Solid debugging skills and the ability to troubleshoot and performance-tune production services.
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Strong SQL and data modeling skills.
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Experience with version control (Git) and CI/CD workflows.
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Comfort using AI coding assistants like Claude Code as part of your workflow, and the discipline to validate outputs (tests, metrics, evaluation) rather than trusting them blindly.
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Strong problem-solving and communication skills, and the ability to collaborate across functions.
Nice to have
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Experience building and maintaining data pipelines (ETL/ELT).
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Exposure to event-driven architectures, cloud deployment on AWS, and containers (Docker/Kubernetes).
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Hands-on experience building or deploying AI/ML-powered features or data-driven products.
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Familiarity with machine learning workflows, including data preparation, training, and deployment.
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Familiarity with ML libraries/frameworks (e.g., scikit-learn, TensorFlow, PyTorch, or similar).
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Exposure to LLMs, NLP, or generative AI use cases.
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Experience with Databricks, Apache Spark, or similar distributed data platforms (including cost monitoring and optimization).
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Experience deploying ML models using MLOps tools (e.g., MLflow, Airflow, Kubeflow).
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Experience with workflow/orchestration tools (Airflow, Argo, Dagster), Terraform/Ansible, and Grafana dashboards.
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Search/retrieval systems (Elasticsearch/Lucene) and GraphQL.
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Understanding of real-time or streaming data pipelines.