Sr Software Engineer

LinkedIn is the world’s largest professional network, built to create economic opportunity for every member of the global workforce. Our products help people make powerful connections, discover exciting opportunities, build necessary skills, and gain valuable insights every day. We’re also committed to providing transformational opportunities for our own employees by investing in their growth. We aspire to create a culture that’s built on trust, care, inclusion, and fun – where everyone can succeed.

Join us to transform the way the world works.

Location: Dublin, Ireland
At LinkedIn, our approach to flexible work is centered on trust and optimized for culture, connection, clarity, and the evolving needs of our business. The work location of this role is hybrid, meaning it will be performed both from home and from a LinkedIn office on select days, as determined by the business needs of the team.

Role Description:
The Platform Data Services (PDS) engineering team within Core AI is responsible for maintaining LinkedIn's Economic Graph, serving diverse use cases across the entirety of the business. PDS is seeking a Senior Software Engineer to lead the development and support of LinkedIn's world-class Geo data products and to drive the creation of the next generation of automatic curation, labeling, and annotation of high-quality data to power use cases across LinkedIn.
As a Senior Software Engineer, you will play a pivotal role in shaping the technical vision and strategy for the team, collaborating with cross-functional partners, and mentoring team members. You will leverage your expertise in building backend and mid-tier systems, APIs, and data processing pipelines to deliver innovative solutions. Your deep understanding of online, nearline, and offline data access paradigms will ensure the scalability, reliability, and performance of critical systems supporting LinkedIn's business.

Key Responsibilities:
• Lead the design, implementation, and delivery of complex, high-impact systems that power LinkedIn's Geo data products.
• Drive architectural decisions and provide technical direction to the team.
• Collaborate with cross-functional teams to define requirements and ensure alignment on deliverables.
• Mentor and guide junior and mid-level engineers, fostering a culture of technical excellence and innovation.
• Ensure operational excellence by implementing best practices for monitoring, performance tuning, and troubleshooting critical systems.
• Stay ahead of industry trends to introduce cutting-edge technologies and methodologies to the team.
• Advocate for and contribute to the technical vision for automatic curation, labeling, and annotation of high-quality data.

Basic Qualifications:
• 5+ years of hands-on industry experience in backend engineering
• 5+ years experience in Java and Scala; knowledge of Python is a plus.
• 5+ Experience with system design, architecture, and working with large-scale data.
• BA/BSc Degree in Computer Science or related technical discipline, or related practical experience.

Preferred Qualifications:
• 7+ years of hands-on industry experience in backend engineering
Familiarity with public cloud platforms and various databases (SQL, NoSQL, key-value stores etc.).
• Technical expertise with data models, data analytics and data processing.
• Experience supporting and/or scaling extremely large and critical services
• Experienced with software engineering best practices and keen for automation (e.g. unit testing, code reviews, design documentation, CI/CD, etc.).
• Familiarity with observability and monitoring tools such as Grafana, Prometheus, and Graphite.
• A team player with excellent communication skills, independent learning capability and curiosity to explore new fields and achieve constant improvement
• Significant experience with Spark and a deep understanding of its ecosystem.
• Experience working with large scale databases & streaming infrastructure services
• Previous experience in Internet scale companies is a plus.

Suggested Skills:
• Technical leadership and mentoring
• Batch applications (e.g., Spark) and data pipelines (e.g., Airflow)
• Streaming technologies (e.g., Kafka, RabbitMQ)
• Working with various databases (SQL, NoSQL, key-value stores)
• Observability and monitoring tools



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