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Senior Data Engineer
2 months ago
Position Summary
- Analyzes complex data structures from disparate data sources and design large scale data engineering pipeline.
- Develops large scale data structures and pipelines to organize, collect and standardize data that helps generate insights and addresses reporting needs.
- Implement data ingestion pipeline using APIs, third party tools, or create custom codes to ingest high volume data into Cloud environment.
- Writes processes, designs database systems and develops tools for real-time and offline analytic processing.
- Collaborates with product business and data science team to collect user stories, translate into technical specifications, and implement data transformation, algorithms and models into automated processes.
- Uses strong programming skills in PySpark, Python/Java or any of the major languages to build robust data pipelines and dynamic systems.
- Builds highly scalable and extensible data marts and data models to support Data Science and other internal customers on Cloud. Integrates data from a variety of sources, assuring that they adhere to data quality and accessibility standards.
- Analyzes current information technology environments to identify and assess critical capabilities and recommend solutions
- Analyzes current information technology environments to identify and assess critical capabilities and recommend solutions.
- Build and facilitate machine learning across large scale systems and campaigns and ensure deployment and updates in partnership with data engineering.
- Develop and participate in presentations and consultations to existing and prospective constituents on analytics results and solutions.
- Interact with internal and external peers and managers to exchange complex information related to areas of specialization.
- The ideal candidate should be detailed oriented with the ability to quickly understand complex situations, manage multiple urgent tasks at the same time and have a proven track record of communicating in an open and honest way that quickly builds trust and respect.
- The ideal candidate should work with data related to a wide range of customer interactions and analytics. This role will be responsible for designing and deploying large scale ML models with support from data engineering and Product Team.
- Experiments with available tools and advice on new tools to determine optimal solution given the requirements dictated by the model/use cases.
Required Qualifications
- 5-6+ years of progressively complex related experience in cloud data engineering and data analysis
- 6+ years of building cloud native analytical products in GCP, Azure or AWS
- Sound knowledge in any of cloud Technology is must preferably Google cloud Platform (GCP)
- 6+ Years of experience in Data Engineering, Analytics and Machine Learning Systems.
- Exposure in implementing Gen AI and/or NLP based solutions using LLMs
- Expertise in Google Cloud Data Services: Big Query, Data Proc, PubSub, Cloud Functions, Cloud Storage, Dataflow, Composer.
- Deep Knowledge of Large Scale distributed Data Architecture and Performance Optimization Techniques.
- Proficiency in developing Complex Data Pipelines, ETLs and Workflows on Cloud Platform optimized for High Volume of Health Care data.
- Proficiency in using Cloud Platforms such as GCP/Azure/AWS. Knowledge of tools like Composer, Kafka, PySpark and SQL
- Knowledge of Programming Language Java/Python.
- Proficiency with CI/CD tooling like Jenkins, GitHub to enable robust development pipelines for data and ML.
- Strong knowledge of large-scale search applications and building high volume data pipelines, preferably using PySpark on GCP and It's native tools such as BigQuery, Airflow, Composer, DataProc, PUB/SUB, DataFlow and Vertex AI
- Strong Foundational Knowledge in Agile Methodologies.
Preferred Qualifications
- Experience with Healthcare domain is highly desirable.
- Deep understanding of data warehousing, data architecture, and data modeling methods & best practices
- Understanding of AI/ML technology Stack.
- Comfortable working experience with large scale LLMs.
- GCP Certifications: Professional Data Engineer.
Education
- Bachelor's degree or equivalent work experience in Mathematics, Statistics, Computer Science, Business Analytics, Data Science, Engineering, or related discipline
- Master's degree Preferred in Computer Science/ML
