Gracenote is the top provider of entertainment information, creating industry-leading databases of TV, movie, and music metadata for entertainment guides, applications and in-car entertainment. Our technology serves billions of requests daily to hundreds of millions of devices around the world. Our customers include innovators like Apple, Twitter, Google, Spotify, M-GO and Hulu, top consumer electronics and cable companies, and leading automotive manufacturers such as Ford and Toyota, throughout the US and the world. Simply put, data provides you with an opportunity to impact the evolution of the entire entertainment industry.
Job Purpose
Develop and enhance our flagship Video, Audio, Automotive and Sports metadata software solutions.
Design applications with a Platform-first mentality where scale, consistency and reliability are at the core of every decision.
Responsibilities
Design, develop, and maintain scalable and robust Big Data pipelines and systems.
Architect and implement solutions for managing & processing large-scale datasets with fast refresh cycles, ensuring high performance, scalability & accuracy.
Collaborate with cross-functional teams, including data scientists, engineers, and product managers, to define and translate business requirements into technical solutions.
Write clean, maintainable, and efficient code following best practices and coding standards.
Conduct design and code reviews to ensure high-quality deliverables and adherence to best practices.
Troubleshoot and resolve complex issues in systems, ensuring reliability, availability, SLA compliance, observability, and minimal downtime.
Participate in the full software development lifecycle, including planning, development, review, testing, and deployment.
Stay up-to-date with emerging technologies and industry trends to continuously improve skills and knowledge.
Mentor and guide junior engineers, fostering a culture of learning and collaboration within the team.
Qualifications
Bachelor’s degree in Computer Science, Engineering, or a related field.
6 to 10 years of professional experience in Big Data engineering, with hands-on expertise in processing large-scale datasets.
Advanced programming skills in Python, Java, or Scala, with a focus on data processing and stream analytics
.Experience of working with distributed data systems such as Spark or Flink.
Deep understanding of distributed storage systems (HDFS, S3, or ADLS) and modern file formats like Parquet, ORC & Arrow.
Strong expertise in Lakehouse architectures and technologies like Delta Lake, Iceberg and data orchestration tools like Airflow, Dagster.
Knowledge of database systems, including NoSQL stores (Cassandra, MongoDB), relational databases (PostgreSQL, MySQL) and SQL.
Working proficiency with Agile development methodologies and CI/CD practices.
Strong problem-solving skills and the ability to work independently as well as in a team environment.
Excellent communication and interpersonal skills.
Preferred Skillsets
Experience with cloud platforms (e.g., AWS, Azure, Google Cloud).Familiarity with containerization technologies (e.g., Docker, Kubernetes).Knowledge of CI/CD tools and practices.
Experience with test-driven development (TDD) and automated testing frameworks.
Familiarity with data visualization tools is good to have.
