Senior Engineer - Hyperscale Analytics

About the Role

We are seeking exceptional Senior Engineer- Hyperscale Analytics to design and scale next-generation data processing and analytics platforms that power OLTP, OLAP, and large-scale distributed data systems. You will build and optimize pipelines and services that handle billions of records daily, enabling real-time transactions, analytical insights, and AI-driven decisioning.

What You’ll Do

• Transactional & Analytical Systems: Design and implement highly scalable OLTP systems for real-time workloads and OLAP systems for complex analytical queries on massive datasets
• Distributed Processing: Build, optimize, and maintain large-scale batch and streaming pipelines using frameworks such as Apache Spark, Flink, Presto/Trino, or Kafka Streams
• System Performance & Scale: Optimize systems for low-latency queries, high-throughput ingestion, and interactive analytics, ensuring seamless performance as data volumes scale to petabytes
• Data Infrastructure: Develop and integrate with modern storage and processing systems (e.g., Snowflake, BigQuery, Redshift, Cassandra, HDFS, Delta Lake, Iceberg) to support hybrid analytical/transactional workloads
• Reliability & Observability: Ensure high availability, reliability, and monitoring across large compute and storage clusters with automated failover and recovery
• Collaboration: Partner with data scientists, ML engineers, and product teams to build unified, secure, and cost-efficient data platforms

What You’ll Bring

• 6+ years of professional software engineering experience, with a significant portion focused on data infrastructure, distributed systems, or large-scale analytics platforms
• Deep experience with distributed data processing frameworks (e.g., Spark, Flink, or similar)
• Strong background in cloud-scale data architecture (AWS, Azure, or GCP) — data lakes, warehouses, streaming platforms
• Proficiency in one or more of: Python, Java, Scala, or Go
• Experience with modern data warehouse/lakehouse technologies (e.g., Snowflake, Databricks, BigQuery, Redshift)
• Solid understanding of data modeling, ETL/ELT design, and pipeline orchestration (e.g., Airflow, dbt)
• Track record of designing for scale, reliability, and cost efficiency in production systems

Bonus Skills

• Experience with HTAP (Hybrid Transactional/Analytical Processing) systems or real-time analytics platforms
• Familiarity with data lakehouse architectures and formats like Parquet, ORC, Delta, Iceberg, Hudi
• Knowledge of containerized deployments (Docker, Kubernetes) and cloud-native data architectures (AWS Redshift, GCP BigQuery, Azure Synapse)
• Background in query engine development or contributing to open-source OLAP/OLTP frameworks