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À propos
Architect, design, and implement scalable data platforms and applications using Azure cloud services including Data Lake Storage, SQL Database, Data Factory, Databricks, and DevOps. Build robust data pipelines and ETL frameworks to integrate structured and unstructured data from diverse sources such as APIs and external feeds, ensuring data quality, consistency, and governance. Develop and optimize big data processing workflows with Apache Spark and PySpark for batch and real-time analytics, supporting enterprise-scale data ecosystems. Design and manage cloud data warehouses on Snowflake, implementing Cortex AI-powered LLM solutions and Streamlit based applications for advanced metadata enrichment and analytics use cases. Create interactive BI dashboards using Power BI to deliver actionable insights for business decision-making and integrated BI solutions with data pipelines and data warehouses for real-time reporting. Utilize Python, SQL, and Java to develop data transformations, backend services, and analytics solutions, while supporting CI/CD and version control through Git. Build and deploy end-to-end data pipeline solutions using PySpark, Apache Phoenix, and Hive to automate insurance claim encounter processing and integrate remediation workflows into enterprise data platforms. Provide advisory and implementation services for large-scale data ecosystems, covering master data, metadata, data architecture, data governance, data quality, and data modeling. Leverage cloud, web-based, and on-premises platforms-to help organizations unlock the value of their technology investments and establish data governance policies with security measures using Azure Key Vault and Azure Active Directory. Architect real-time data streaming pipelines using Apache Kafka, ensuring high availability and fault tolerance, and optimize queries and data processing jobs for performance, scalability, and cost efficiency. Design and develop data transformation pipelines using Azure Data Factory, Databricks, and Synapse to cleanse, enrich, and prepare data for analytics and reporting. Develop proof-of-concept on AWS EMR for on-demand cluster provisioning and auto-termination using AWS Lambda and S3, optimizing compute costs and enhancing workflow automation. Implement data lake and data warehouse solutions on AWS using S3, EMR, Lambda, and Step Functions, enabling seamless integration of structured and semi-structured data. Provide technical leadership and mentorship to junior engineers, collaborate with cross-functional teams, and stay updated on emerging technologies and best practices in modern data engineering. Qualifications
Job requirements:
Bachelor's degree in Computer Science, Computer Information Systems, or Engineering related or Technical related fields plus 5 years of progressively responsible post-baccalaureate experience. Foreign degree equivalent is acceptable. In lieu of the above, we will also accept a Master's degree in Computer Science, Computer Information Systems, or Engineering related or Technical related fields plus 2 years of experience. We will also accept any suitable combination of education, training and/or experience. Experience should include at least 2 years experience working on Data Lake; Data Factory; Databricks; DevOps; PySPARK; Power BI; API; Python; SQL; Git; Apache Spark; Snowflake; Apache Phoenix; Hive; AWS EMR; AWS Lambda; S3; Azure Synapse Analytics; Apache Kafka; ETL. Hours
HOURS : M-F, 8:00 a.m. 5:00 p.m. Job Location
JOB LOCATION : Dallas, Texas. Travel is not required, but candidates must be willing to relocate to unanticipated locations across the country per contract demand. Contact
CONTACT : Email resume referencing job code# SDE01132026ANB to Maruthi Technologies Inc. DBA Anblicks at recruitment@anblicks.com #J-18808-Ljbffr
Compétences linguistiques
- English
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