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Lead GCP Data Engineer

4 months ago

delhi, India EXL Full time
As a Lead GCP Data Engineer, you will be responsible for data 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 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.
Work Location – Pune (Hybrid)
Experience - 8 to 10 Years
Must have skills:
8+ years of proven experience as a Lead in Big Data Engineering .
Mandatory Technical Proficiency - Hands-on experience with Python , PySpark , Google Cloud Full Stack , 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 ecosystems 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,
Knowledge of Snowflake , ML - Machine Learning, Gen AI (LLMs), Marketing activation.
Role & Responsibilities:
Accountable for overall technical implementations and management of respective engineering POD
Liaise with Foundry on solutions design; Agile Project Manager on planning & estimation; Engineers on task assignment; Business stakeholders on prioritization
Streamline workflows and orchestrate Data pipelines .
Automate deployments and facilitate MTQ/MTP through CI-CD/DevOps
Conduct code reviews, automate regression tests, and facilitate data reloads.
Participate in requirements gathering and architectural discussions.
Responsible for the Overall Quality of Engineering builds and Architecture.
Technical Leadership: Lead creation of technical design/specifications and provide technical leadership and guidance to development teams, promoting best practices. Provide expertise in 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 artifacts (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 the 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 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