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GCP Data Engineer
4 weeks ago
Position – GCP Data Engineer – Senior
Work Location – Hybrid – all EXL locations. Will involve relocation for selected outstation candidates.
Experience - 5 to 8 Years
Must Have:
- 5+ years of proven experience in Big Data Engineering .
- Mandatory Technical Proficiency - Hands-on experience with Python , PySpark , Google Cloud Full Stack , SQL, Could run (Docker), 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, Pub/Sub, Cloud Shell, 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 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:
- Data ingestion & cleansing from sources systems (files, DBs, APIs)
- Harmonize, integrate, and centralize data to BigQuery , Data warehouse
- Calculations, derivations, and aggregations in BigQuery , Data mart
- Integrate with Master Data Services , automate data quality rules
- Data exposition (API ) with data-as-a-service
- Automate deployments and facilitate MTQ/MTP through CI-CD/DevOps
- Conduct code reviews, automate regression tests, facilitate data reloads
- To be upskilled if not qualify: DBT , Terraform , Databricks
- Streamline workflows and orchestrate Data pipelines
- 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.
- 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 data solutions adhere to security and compliance standards, implementing necessary controls and encryption mechanisms.
- Documentation : Create and maintain comprehensive technical documentation to support the development and maintenance of data solutions.
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