Data Engineer

We are looking for a Data Engineer Origin Data Product (ODP) Support with strong hands-on experience in modern data platforms to maintain, support, and optimise data pipelines at scale.

The role focuses on ensuring the reliability, quality, and operability of Origin Data Products, which are untransformed, governed copies of source data enriched with metadata and lineage. These products form the foundation for downstream analytical and operational use cases.

You will play a pivotal role in enabling trusted, discoverable, and reusable data assets, while supporting end-to-end data engineering activities including ingestion, processing, modelling, and access across GCP platforms.

Technical Skills :

Strong hands-on experience with GCP ecosystem (BigQuery, Dataflow, Composer/Airflow, Cloud Storage, Cloud Run)

Proficiency in SQL, Python / PySpark for data engineering and automation

Experience in supporting data pipelines, including monitoring and troubleshooting

Hands-on experience with DBT (Data Build Tool) for data transformation and modelling, enabling modular, reusable, and testable data pipelines

Knowledge of data ingestion frameworks (e.g., EasyIngest or similar)

Familiarity with CI/CD, GitHub, and Terraform

Experience with LBG schedulers (e.g., TWSD) and cloud-native orchestration





Strong understanding of DevOps practices and software engineering principles in GCP environments

Knowledge of BigQuery architecture patterns and cloud database systems (SQL/PostgreSQL)

Exposure to data governance, cataloguing, and lineage tooling (e.g., Collibra)

Proven experience in production support, covering Incidents, Change Requests (CRs), and Problem Management (PRBs)

Desirable Skills/Valuable to have :

Experience working with Adobe Analytics data ingestion into GCP (e.g., Data Streams / batch exports into BigQuery) and building scalable event-level datasets

Understanding of clickstream data modelling, including sessionsation, event hierarchy, and handling high-volume event data at scale

Familiarity with tagging frameworks (e.g., Tealium, Adobe Launch) and how front-end implementation impacts downstream data quality and schema design

Ability to interpret and validate tagging specifications, and identify misalignments between implemented vs expected tracking (a known gap in practice)

Understanding of identity stitching concepts (e.g., visitor ID, customer ID, cross-device tracking) and their implications on data models

Awareness of privacy, consent, and regulatory considerations (e.g., GDPR) in digital analytics data collection

📌 Gcp Data Engineer (Bengaluru)
🏢 Infinityquest It Services
📍 Bengaluru