Data Engineer 2

Consumers care first and foremost about having their time valued by brands. Brands need insights into their customer service operation to serve their consumers effectively. Such insights and analytics are delivered through various data products like in-app analytics dashboards and data-sharing integrations. The data platform team is responsible for designing, building, and maintaining the data infrastructure that enables such data and analytics products at scale. We build and manage data pipelines, databases, and other data structures to ensure that the data is reliable, accurate, and easily accessible. We also enable internal stakeholders with business intelligence and machine learning teams with data ops. This team manages the platform that handles 2 million events per minute and processes 1+ terabytes of data daily.

Responsibilities:

• Design and build scalable backend systems that power data-intensive and analytics-driven use cases.
• Develop high-performance data access layers and services, enabling efficient querying, transformation, and serving of large datasets.
• Work with modern platforms like Snowflake to build and optimise data workflows.
• Build and contribute to intelligent data products, leveraging GenAI/LLM capabilities for use cases like anomaly detection, summarisation, and insights generation.
• Apply strong data modelling practices to design clean, scalable, and maintainable data schemas for analytics and product use cases.
• Collaborate closely with product, analytics, and business stakeholders to translate requirements into robust technical solutions.
• Own end-to-end system design including architecture, performance, scalability, reliability, and security considerations.
• Continuously improve system performance through query optimisation, caching strategies, and efficient data access patterns.
• Contribute to real-time and near-real-time data processing systems where required.
• Write clear design documents, conduct code reviews, and ensure high engineering standards.
• Mentor team members on backend engineering best practices, system design, and modern data platform usage.

Requirements:

• 4+ years of experience in backend engineering, building scalable, reliable, and high-performance systems in production environments.
• Strong proficiency in at least one high-level programming language (Python, Java, or similar) with solid grounding in data structures, algorithms, and problem-solving.
• Strong working knowledge of SQL, including query optimisation, performance tuning, and working with large-scale datasets.
• Hands-on experience with relational and/or analytical databases (e. g., Snowflake, Redshift, PostgreSQL, MySQL, etc. ), including data modelling and schema design.
• Good understanding of distributed systems fundamentals, including scalability, fault tolerance, and system design.
• Familiarity with cloud platforms (AWS/GCP/Azure) and modern data infrastructure concepts.
• Exposure to data-intensive applications, such as high-throughput APIs, event-driven systems, or analytics platforms.
• Interest in or experience with modern data platforms (e. g., Snowflake) and willingness to quickly learn data engineering tooling and paradigms.
• Basic understanding of security, networking, and production system operations (monitoring, observability, reliability).
• Exposure to or strong interest in GenAI/LLM capabilities (e. g., embeddings, vector search, prompt engineering, or LLM-powered applications) is highly desirable o
• Experience with streaming or real-time systems (Kafka, Kinesis, etc. ) is a plus.
• Experience with data pipeline technologies (Spark, Airflow, etc. ) is a plus.
• Data visualisation skills are a plus (PowerBI, Metabase, Tableau, Hex, Sigma, etc).
• Bachelor's Degree in Computer Science (or equivalent).
• Strong verbal and written communication skills.