Tech Stack

Description

You will own and evolve large-scale, business-critical data platforms that power 6sense's AI-driven products. You'll design, build, and operate high-throughput data pipelines, drive architectural direction, ensure data reliability and quality, and collaborate across teams to translate business needs into scalable solutions.

Requirements

• 10+ years of experience building backend or data intensive systems.
• Proven ownership of mission critical data platforms in production.
• Experience operating at Senior or Staff engineer scope.
• Understanding of AI-native software development practices, including automated code generation, AI-driven testing, and human-in-the-loop review systems.
• Proven ability to adopt and drive modern engineering paradigms, particularly integrating AI into development workflows.
• Strong programming skills in Python and SQL.
• Deep experience with big-data and streaming systems such as Spark, Kafka, Flink, Hive, Trino/Presto.
• Solid understanding of distributed systems, data consistency, and fault tolerance.
• Hands-on experience with AWS based data infrastructure.
• Comfortable working in ambiguity and making high impact technical decisions.
• Strong intuition for data modeling, system design, and long term maintainability.
• Ability to influence teams without formal authority.
• Experience using AI tools (e.g., Copilot, ChatGPT, Cursor) to improve productivity in design, coding, and debugging.
• Strong judgment on validating correctness and performance.

Responsibilities

• Lead Large-Scale Data Systems: Design, build, and evolve high-throughput batch and streaming data pipelines processing billions of records; own core datasets powering product features, analytics, and ML systems; build systems for enrichment, deduplication, identity resolution, and confidence scoring at scale.
• Drive Architecture Technical Direction: Define long-term architecture for foundational data platforms; lead complex, multi-quarter initiatives such as platform migrations, major refactors, and new data foundations; balance data correctness, freshness, cost, and performance.
• Ensure Reliability Data Quality: Own SLAs/SLOs for data freshness, accuracy, completeness; design data quality frameworks, reconciliation mechanisms, and observability; lead root-cause analysis and long-term fixes for production issues.
• Collaborate Across the Company: Partner with Product, Analytics, Data Science, Platform, and GTM teams to translate business needs into scalable data solutions; act as technical point of contact for third-party data integrations and vendors.
• Raise the Engineering Bar: Mentor senior engineers and influence technical standards; lead by example in design reviews, operational ownership, and incident response; help shape engineering best practices around scalability, reliability, and maintainability.
• Leverage generative AI and AI coding tools to accelerate developer productivity, engineering workflows, and automation of repetitive tasks.