GCP Data Architect

7 days ago

Delhi, Delhi, India EXL Full time

As a GCP Data Engineer Architect, you will be responsible for architecting enterprise data solutions for migration, transformation or modernization on Google Cloud Platform (GCP) integrating native GCP services and other 3rd party data solutions.

You will collaborate with cloud partners and our cross-functional teams to enable sector-specific use cases.

Your responsibilities will involve technology adoptions pertaining to AI/ML, Generative AI, Advanced analytics, serverless and service driven ecosystem for data led and AI enabled digital transformations.

Expert proficiency in comprehending the principles for large scale architecting, solutioning and operationalization of data pipelines, warehouses, data lakes, analytics platforms and activation services on GCP is crucial.

We are looking for professionals with solid experience on design, delivery, and implementation of GCP infrastructure and solutions along with 3rd party technologies on Google Cloud.

Position

– GCP Data Engineer - Architect
Work Location

– Pune (Hybrid)
Experience

-12+ Years

Must have skills:
12+ years

of proven experience as an Architect in

Big Data Engineering .
Mandatory Technical Proficiency - Hands-on experience with

Python ,

PySpark ,

Google Cloud Full Stack Architecture ,

SQL ,

DevOps, Databricks, DBT & Terraform .
Experience with

Google Cloud Services

such as Streaming

  • Batch, Cloud Storage, Cloud Dataflow ,
Data Proc ,

Big Query

&

Big Table
Proven real time exposure and use of contemporary data mining, cloud computing and data management ecosystem like Google Cloud,

Hadoop , HDFS and

Spark .
Proficient in

Data Modelling

that can represent complex data structures while ensuring accuracy, consistency, and efficiency; data warehousing, and

ETL
processes.
Ability to perform system analysis and assessment of existing

Systems Design

and operating methodologies leveraging in-depth knowledge of big data technologies and ecosystems.
Experience with

GitHub ,

Anaplan ,

Looker

&

Power BI ,
Excellent problem-solving skills and the ability to address complex technical challenges.
Strong communication and leadership skills.

Good to have:
Experience with

Apigee ,

Apollo GraphQL .
Experience with

Quality Assurance ,

Agile ,

Documentation

&

Presentation .
Experience with serverless

Data warehousing

concepts,

Additional programming/scripting languages:
JavaScript ,

Java .
Knowledge of

Snowflake ,

ML

  • Machine Learning ,
Gen AI

( LLMs ), Marketing activation.

Role & Responsibilities:

Solution Design:
Participate in in requirements gathering and architectural discussions. Define technical requirements and create solution designs that align with business goals and objectives.
Responsible for Overall quality of Engineering builds and Architecture.
Overall Solutions architecture of

Data foundations

& use-cases on

BTDP

Technical Leadership:
Lead creation of technical design/specifications and provide technical leadership and guidance to development teams, promoting best practices. Provide expertise with

Master Data Management , Reference Data Management,

Data Quality , Meta Data Management and

Data Governance

in General.

Programming Hands on:
Develop and maintain software components using

Python ,

PySpark , and

GCP services

to process and analyze large datasets efficiently. Build data pipelines and perform data transformations.
Enforce consistent standards and quality through end-to-end review of all artefacts (incl. BRD, HLD, LLD, Data Models, STTMs, UI-UX, NFRs, Code, Test, PBI architecture design.
POC and Pilot of new reusable frameworks, tools & technologies.
Evaluate newest technologies for optimization opportunities and future enhancement needs like self-serve and ad hoc reporting. Implement necessary infrastructure for optimal and efficient

ETL
from a disparate data.

Performance Optimization:
Identify and address performance bottlenecks, ensuring the system meets required throughput and latency targets.

Security and Compliance:
Ensure that data solutions adhere to security and compliance standards, implementing necessary controls and encryption mechanisms.

Scalability:
Architect scalable and highly available data solutions, considering both batch and real-time processing.

Documentation:
Create and maintain comprehensive technical documentation to support the development and maintenance of data solutions.

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