Senior Software Engineer - Data

Location and Work Arrangement

• Candidates are preferred to be based in Bangalore, India.
• The role is expected to comply with the team’s hybrid work schedule requirements.

Data Engineering Context

Role Overview

Data Platform Responsibilities

• Drive the end-to-end design and evolution of data models, pipelines, and data products for Search, Recommendations, and Marketing.
• Own scalable batch-first systems that transform structured, semi-structured, and unstructured data into AI-consumable representations.
• Design reliable pipelines optimized for cost, performance, retrieval, freshness, and contextual usability.
• Build and maintain fact and dimension models, slowly changing dimensions, change data capture pipelines, and data versioning strategies.

Semantic Data and Governance

• Contribute to a unified semantic layer connecting raw data with AI and machine-learning systems.
• Work with metadata, lineage, and data discovery frameworks to improve transparency, governance, and usability.
• Define and enforce data modeling standards, data contracts, and data quality frameworks across teams.
• Drive improvements in data observability, SLA/SLO adherence, and pipeline reliability.

Collaboration and Architecture

• Partner with Product, Analytics, Data Science, Application, and Data Engineering teams to translate ambiguous problems into reusable data solutions.
• Make architectural decisions and trade-offs across storage, compute, and orchestration layers within a GCP-native stack.

Education and Experience

• A Bachelor’s or Master’s degree in Computer Science or a related field, or equivalent experience, is required.
• Approximately 12 years of experience in data engineering and ownership of large-scale data platforms and datasets is required.

Core Data Engineering Skills

• Deep expertise in data modeling, including dimensional models, slowly changing dimensions, and wide tables.
• Strong understanding of change data capture, data versioning, and incremental processing strategies.
• Strong experience with Google Cloud Platform, Google BigQuery, and Google Cloud Storage.
• Advanced SQL and Python proficiency focused on optimization, cost efficiency, and large-scale processing.
• Understanding of data lakehouse principles, Parquet, partitioning, clustering, and performance tuning.

Additional Technical Skills

• Experience with production-grade ingestion, transformation, serving, data quality, and observability practices.
• Experience with event-driven and streaming architectures such as Pub/Sub or Kafka.
• Experience preparing data for AI/ML use cases, feature engineering workflows, and semantic or context-rich data layers.
• Familiarity with metadata management, data lineage, and data discovery.