Event Streaming Developer

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.
 

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