Senior Software Engineer

Job Description:

Responsibilities

• Drive the end-to-end design and evolution of data models, pipelines, and data products for Search, Recommendations, and Marketing—operating at scale and influencing multiple domains.
• Own the development of scalable, batch-first data systems that ingest and transform structured, semi-structured, and unstructured data into high-quality, AI-consumable representations (e.g., curated datasets, embeddings-ready data, feature layers, and semantic abstractions).
• Design and build high-fidelity data pipelines optimized for reliability, cost, and performance, with a focus on efficient retrieval, data freshness, and contextual usability for downstream systems.
• Build and maintain robust data models including fact/dimension models, SCDs, CDC pipelines, and data versioning strategies to ensure consistency and reproducibility.
• Contribute to the development of a unified semantic layer that bridges raw data and AI/ML systems, enabling standardized metrics, reusable data definitions, and improved data access patterns.
• Work with metadata, lineage, and data discovery frameworks to improve transparency, governance, and usability of data across the organization.
• Partner cross-functionally with Product, Analytics, and Data Science to translate ambiguous business problems into well-defined data solutions and reusable data assets.
• Define and enforce data modeling standards, data contracts, and quality frameworks across teams.
• Drive improvements in data observability, SLA/SLO adherence, and pipeline reliability across the ecosystem.
• Make architectural decisions and trade-offs across storage, compute, and orchestration layers within a GCP-native stack.

Qualification & Experience     

• Bachelor’s/Master’s degree in Computer Science or related field, or equivalent experience.
• 12 years of experience in Data Engineering, building and owning large-scale data platforms and datasets at scale.
• Deep expertise in data modeling (dimensional models, SCDs, wide tables), along with strong understanding of CDC, data versioning, and incremental processing strategies.
• Strong experience designing and building data pipelines on Google Cloud Platform using Google BigQuery and Google Cloud Storage.
• Advanced proficiency in SQL and Python, with a strong focus on query optimization, cost efficiency, and large-scale data processing.
• Solid understanding of data lakehouse principles, storage formats (e.g., Parquet), partitioning, clustering, and performance tuning.
• Experience building reliable, production-grade data systems, including ingestion, transformation, serving layers, and strong data quality and observability practices (SLAs/SLOs).
• Experience with event-driven and streaming architectures (e.g., Pub/Sub, Kafka), with the ability to apply them pragmatically alongside where needed.
• Experience enabling AI/ML use cases from a data perspective, including preparing high-quality datasets for model consumption, supporting feature engineering workflows, and building semantic or context-rich data layers that improve downstream usability.
• Familiarity with concepts such as metadata management, data lineage, and data discovery, and their role in improving trust and usability of data platforms.
• Proven ability to translate ambiguous business requirements into scalable data models and systems, especially in domains like search, recommendations, or marketing analytics.
• Demonstrated ownership of large problem spaces end-to-end, with the ability to influence architecture, drive standards, and align multiple teams.
• Experience providing technical leadership and mentorship, setting best practices, and raising the bar for engineering quality.
• Strong communication skills with the ability to articulate technical decisions, trade-offs, and system designs to diverse stakeholders.
• Understanding of NoSQL Database exposure.
• Familiarity with Bl tools like Looker, Tableau, AtScale, PowerBI, or any similar tools.