**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

📌 Event Streaming Developer (Bengaluru)
🏢 Burns & McDonnell
📍 Bengaluru