Description

Google Cloud

• Architecture Mindset
• Hands-On Pipeline & Production Builder

Experience Required 7-10 years

We are building a central Data Engineering team responsible for ingesting data from enterprise source systems and engineering applications - including Oracle, CRM platforms, and internal engineering tools - loading it into our data platform and delivering foundational data products that serve Group Practices across the firm. We're looking for a senior engineer who combines an architecture mindset with deep hands-on experience - someone who can design scalable systems and personally build and ship them into production. You will own pipelines end-to-end, from whiteboard to running code, on Google Cloud.

What You'll Do

ARCHITECTURE & DESIGN

Architect end-to-end data platform solutions: from source system connectivity through ingestion, storage, transformation, and data product delivery - making deliberate decisions on scalability, cost, and maintainability.

Define reference architectures and reusable patterns: for pipelines, storage layout, IAM boundaries, and CIM modeling that the whole engineering team builds from.

Evaluate and select Google Cloud services: for each workload: streaming vs. batch, event-driven vs. scheduled, structured vs. unstructured.

Translate business requirements: from Portfolio Data Managers and domain data owners into clear, implementable technical designs.

HANDS-ON PIPELINE & PRODUCTION ENGINEERING

Personally build and ship production pipelines: this role codes, not just designs. You will own your work from first commit through deployment, monitoring, and ongoing reliability.

Build end-to-end ingestion pipelines: from source systems (Oracle, CRM, engineering applications) into Google Cloud Storage and BigQuery.

Build multimodal unstructured data processing workflows: handling PDFs, Excel, PowerPoint, audio recordings, video files, and engineering drawings.

Apply Google Cloud AI services: Document AI, Speech-to-Text, Video Intelligence, Vision API, Vertex AI - to extract structured information from unstructured content and ship results to production datasets.

Use LLMs: to classify, summarize, and transform unstructured text and documents into structured, queryable data products.

Build and maintain Common Information Model (CIM) semantic layers: aligning ingested data with business-domain meaning across Group Practices.

Design and manage Google Cloud Storage buckets: lifecycle policies, folder structure, access controls, and cost optimization.

Configure and enforce Google Cloud IAM: service accounts, role bindings, workload identity, and least-privilege policies across all platform resources.

Write observability into every pipeline: logging, alerting, data quality checks, and SLA monitoring from day one.

Required Skills

ARCHITECTURE MINDSET & PRODUCTION ENGINEERING

Proven record of designing systems and then personally building them for production, not delegating implementation after the whiteboard session.

Architecture trade-off decisions: streaming vs. batch, managed vs. custom, normalized vs. denormalized - and the ability to explain the reasoning clearly.

Reliability patterns: idempotency, dead-letter queues, retry logic, schema evolution, and data quality gates.

Technical design documents that a team can build from, and are equally comfortable writing the code.

GOOGLE CLOUD PLATFORM

BigQuery

• Cloud Storage (GCS)
• Cloud IAM
• Dataflow / Apache Beam
• Cloud Composer / Airflow
• Pub/Sub
• Vertex AI
• Cloud Functions / Run

UNSTRUCTURED DATA & AI TRANSFORMATION

Google Document AI

• Speech-to-Text API
• Video Intelligence API
• Vision API / OCR
• PDF & Office file parsing
• Audio processing
• Engineering drawing extraction
• LLM-based data extraction

DATA ENGINEERING FUNDAMENTALS

Python

• SQL (BigQuery dialect)
• dbt
• ELT / ETL pipeline design
• Data modeling
• Git / CI-CD
• Terraform / IaC

SECURITY & GOVERNANCE

Google Cloud IAM: service accounts, custom roles, workload identity federation, least-privilege design across projects and datasets.

GCS access controls: uniform bucket-level access, IAM conditions, VPC Service Controls, audit logging.

DATA MODELING & CIM

Common Information Model (CIM): experience building CIM semantic layers - ideally in energy, utilities, or infrastructure domains (IEC 61968 / 61970).

Source-aligned data products: faithful to source systems, enriched with business-domain meaning ("zero layer").

Dimensional modeling: medallion architecture (bronze / silver / gold), and structuring data for downstream BI and AI consumers.

Qualifications

The candidate should have a BCA / MCA / B.TECH degree in Information Technology. The candidate should have a work experience of 7-10 years.

This job posting will remain open a minimum of 72 hours and on an ongoing basis until filled.

Job Information Technology

Primary Location India-Karnataka-Bengaluru

Schedule: Full-time

Travel: No

Req ID: 265146

Job Hire Type Experienced Not Applicable #BMI N/A