Data Engineer

Talent Monitor Bangalore; Gurugram District; On-site; INR 1,200,000 - 2,200,000; Full time

Job summary

Talent Monitor Bangalore is seeking a hands-on Data Engineer to join our growing team in Gurugram. You will own design, build, and optimisation of data pipelines bringing data from diverse sources into GCP to feed critical AI and analytics workflows.

You should be a GCP-native engineer with deep expertise in BigQuery, Dataflow, and related tooling, capable of delivering scalable ETL/ELT pipelines and enforcing data quality across layers.

Qualifications

• Hands-on data engineering experience
• Strong GCP expertise: BigQuery, GCS, Dataflow
• Experience designing and building ETL/ELT pipelines at scale
• Experience with Medallion architecture (Bronze / Silver / Gold)
• Data integration across heterogeneous sources (on-prem, APIs, cloud)
• Proficiency in Python and/or SQL for pipelines
• Config-driven pipeline design and parameterised frameworks
• Cross-cloud data movement (Azure/GCP)
• Familiarity with APIM and identity federation patterns

Responsibilities

• Design and implement scalable data ingestion pipelines from diverse sources
• Build Bronze-to-Silver transformation pipelines per Medallion architecture
• Optimise BigQuery layout through partitioning and clustering
• Implement workload identity federation and APIM integrations
• Embed data quality checks with governance teams
• Participate in design reviews and architecture planning
• Document data flows and decisions for knowledge transfer

Skills

Hands-on data engineering

GCP expertise

ETL/ELT design

Medallion architecture

Data integration

Python/SQL

Config-driven pipelines

Cross-cloud data movement

APIM/workload federation

Tools

BigQuery

Dataflow

GCS

Azure Databricks

c3.ai

Job description

The Role :-

We are looking for a hands‑on Data Engineer to join our growing team. You should be a GCP-native engineer, deeply proficient in the Google Cloud tooling stack, owning the design, build, and optimisation of data pipelines that bring data from a range of source systems into GCP to feed critical AI and analytics workflows. This is a deeply technical role. You will be expected to get hands‑on from day one.

What You Will Do

• Design and implement scalable data ingestion pipelines from a variety of systems including on-premise, Azure Databricks and c3.ai
• Build and maintain Bronze-to-Silver transformation pipelines following the Medallion architecture, using a config‑driven approach
• Optimise BigQuery data organisation through effective partitioning, clustering, and schema design for large‑scale, high‑performance querying
• Implement and maintain workload identity federation and APIM integrations for secure cross‑platform data movement
• Collaborate with Data Governance teams to embed data quality checks within various layer transformations, targeting defined quality thresholds
• Contribute to pipeline design reviews and actively participate in medallion architecture planning sessions with platform and architecture teams
• Document data flows, transformation rules, and design decisions to support knowledge transfer and team continuity

Must-have skills

• Hands‑on data engineering experience
• Strong GCP expertise: BigQuery (partitioning, clustering, optimisation), GCS, Dataflow or equivalent
• Proven experience designing and building ETL/ELT pipelines at scale
• Experience with Medallion architecture (Bronze / Silver / Gold layers)
• Data integration experience across heterogeneous sources on‑premise databases, APIs, cloud platforms
• Proficiency in Python and/or SQL for pipeline development
• Experience with config‑driven pipeline design and parameterised frameworks Strong advantage
• Experience with cross‑cloud data movement (eg Azure GCP)
• Familiarity with APIM, workload identity federation, or service account impersonation patterns

How You Will Work

• Embedded with the client's platform and architecture team in an agile delivery model
• Close collaboration with Data Governance, EA, and AI platform teams
• Expected to operate independently on technical tasks while contributing to broader design discussions
• Resource substitutions are permitted but subject to prior review and approval by the client team