Senior Data Engineer - L4

Location: Vasanthanagar

Candidates for this position are preferred to be based in Bangalore, India and will be expected to comply with their team's hybrid work schedule requirements.

About the Role   :
As a Senior Data Engineer, you will be part of the Data Engineering team with this role being inherently multi-functional, and the ideal candidate will work with Client Experience, Data Scientist, Analysts, Application teams across the company, as well as all other Data Engineering squads  are looking for someone with a love for data, handling ambiguous requirements and the ability to iterate quickly.

Successful candidates will have strong engineering skills and communication and a belief that data-driven processes lead to phenomenal products.

What you'll do:
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.

What You'll Need:
Bachelor’s/Master’s degree in Computer Science or related field, or equivalent experience.
~11 years of experience  in Data Engineering, building and owning large-scale data platforms and datasets p 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 Big Query 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.