Gracenote, a Nielsen company, is dedicated to connecting audiences to the entertainment they love, powering a better media future for all people. Gracenote is the content data business unit of Nielsen that powers innovative entertainment experiences for the world’s leading media companies. Our entertainment metadata and connected IDs deliver advanced content navigation and discovery to connect consumers to the content they love and discover new ones.As a Lead Data Science Engineer on the Gracenote Media team, you will be responsible for defining the AI/ML strategy, overseeing large-scale data science projects, and leading teams to build cutting-edge machine learning solutions that scale content understanding and generation, entity linkage, and more to achieve the scale that matches our customer’s demands.
We are seeking a highly autonomous and strategic Senior MTS (P3) to architect, scale, and take full ownership of our PostgreSQL database infrastructure. At this level, you will not just maintain databases; you will drive our data architecture, define best practices for High Availability (HA) and Disaster Recovery (DR), and lead cross-functional initiatives to ensure our systems are resilient, performant, and scalable. This role requires a proactive problem-solver who bridges the gap between complex application logic and optimal database design, while leveraging modern cloud infrastructure and AI technologies to future-proof our stack.
Key Responsibilities
Architectural Leadership: Lead the design and deployment of highly scalable, distributed database architectures that support complex, high-throughput application requirements.
Infrastructure Reliability & HA: Design, implement, and manage High Availability (HA) architectures. Configure and maintain streaming & logical replication, clustering, and automated failover mechanisms. Proactively identify single points of failure and engineer automated failover mechanisms.
Disaster Recovery (DR): Develop, execute, and test robust disaster recovery plans. Ensure data durability through automated backup strategies and rapid point-in-time recovery (PITR) procedures.
Advanced Query & Performance Engineering: Perform deep-dive performance tuning. Analyze complex execution plans, optimize locking behaviors, and design and implement advanced partitioning and indexing strategies.
Cloud & Infrastructure Management: Drive the deployment, security, and cost-optimization of PostgreSQL within modern cloud environments (AWS, GCP, or Azure), utilizing infrastructure-as-code (IaC).
End-to-End Ownership: Take full accountability for database health, migrations, and data integrity from the development phase through production deployment.
Cross-Functional Mentorship & Ownership: Act as the ultimate owner of database health. Collaborate closely with software engineering teams to influence backend code, guide API data access patterns, and mentor junior engineers on database best practices.
AI Integration & Tech Exploration: Lead the evaluation and integration of emerging technologies, including NoSQL data stores and AI/ML vector databases (e.g., pgvector), to solve complex business problems.
Complex Problem Solving: Act as a key technical escalation point. Leverage strong analytical skills to diagnose deep systemic issues, perform rigorous root-cause analysis (RCA), and implement highly scalable, permanent solutions for production bottlenecks.
Experience: 8 - 12+years of proven, hands-on professional experience working extensively with PostgreSQL in production environments.
Resiliency & Cloud Mastery: Expert-level knowledge of replication strategies, HA architectures, and DR planning. Proven track record managing cloud-based/on-prem (e.g., RDS, Aurora, Cloud SQL) data infrastructure
Core Database Expertise: Deep command of relational database design, ACID transactions, MVCC (Multi-Version Concurrency Control), and advanced SQL querying.
Programming & Automation: Solid proficiency in at least one backend or scripting language (Python, Node.js, Java, Go, or Bash) to build automation tools and integrate with CI/CD pipelines.
End-to-End Execution: Demonstrated ability to independently manage large-scale projects from conception to production, making high-level architectural decisions and defending them with data.
Problem-Solving Mastery: Exceptional troubleshooting skills with a proven track record of deconstructing complex, ambiguous infrastructure challenges and delivering elegant, data-backed solutions.
Terraform & EC2 Hands-On Expertise: Deep, practical experience using Terraform to provision and manage cloud infrastructure. Must have strong hands-on experience deploying, tuning, and managing self-hosted PostgreSQL clusters directly on AWS EC2 instances, including storage optimization (EBS volume types, IOPS tuning), network configuration (VPCs, Security Groups), and custom AMIs.
Preferred Qualifications
NoSQL Exposure: Familiarity with NoSQL databases (e.g., MongoDB, Redis, Cassandra, DynamoDB) and a solid understanding of when to leverage NoSQL versus relational data models.
AI Acumen: Hands-on experience integrating database systems with AI pipelines, or utilizing AI-driven tools to enhance developer productivity and database operations..
Modern Tech Adaptability: A strong appetite for exploring new tech stacks and frameworks, with a history of successfully introducing new, impactful technologies to a team.
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