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Developing and optimizing Spark-based data processing applications on the AWS Databricks platform. Design, build, and test data pipelines and workflows using AWS Databricks. Troubleshooting and debugging data-related issues on the AWS Databricks platform.
$140,000 - $230,300 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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This role requires a seasoned professional with expertise in Databricks on AWS, Terraform, and a strong background in HighSpark, Python, and SQL. The ideal candidate will have experience in data lakes, data warehouses, and optimizing queries to minimize costs.
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You will gain exposure to many facets of the ABS and RMBS spaces, combining information from a variety of sources (loan-level transaction data, deal documents, trustee reporting, etc.) You are able to identify key metrics and transactional details on financial statements, investor reports, and securitization trustee reports and are not afraid to get your hands dirty by diving into loan-level data.
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The qualified candidate will report to the Head of Clinical Data Science and is expected to have the following responsibilities:· Perform integrative analyses of multi-omics data from clinical trials and research cohorts, including genomics, transcriptomics, proteomics, and imaging, to identify biomarkers related to treatment mechanism and response, and potential therapeutic targets.
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In-depth knowledge of Google Cloud Platform (GCP) and its core data services (GCS, BigQuery, Cloud Storage, Dataflow, etc.) Manage and optimize data storage solutions on GCP, including Big Query, Cloud Storage, and Cloud SQL, while implementing data security and access controls using GCP's Identity and Access Management (IAM) and Cloud Security Command Center.
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And familiarity with relevant frameworks and libraries for AI and big data, such as TensorFlow, Apache Spark, Databricks or Snowflake. Develop and execute a data engineering architecture for document AI engineering projects to streamline documentation classification, extraction, enrichment workflows, reducing manual efforts, and improving efficiency.
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Defining and monitoring CDs and process control monitor data in the foundry, correlating it with material parameters and wafer level performance data. Making technical contributions to projects with time-sensitive market entry requirements.
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Create and maintain documentation as required (e.g. data models, data lineage, metadata, data dictionary, information supply chain) Collaborate with the team's stakeholders to identify requirements and deliver insightful, efficient, and secure data and visualization solutions that also align with the Bank's risk appetite, technology standards, and best practices.
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Pre-process and support queries using Hive, Impala, Spark, Presto and Pig. Design and implement data modeling using hive/presto, Hue and Erwin Data Modeler. Experience in writing complex SQL queries, and high-performance, reliable and maintainable code; Experience with Python development for data analysis and data modeling; Experience with Hadoop, including Hive, Spark, Impala, Pig, and Oozie; and Experience with AWS/Cloud components, including EC2, S3, and EMR.
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2+ years of experience with Tableau or other data visualization tools (e.g. Power BI, Looker, Datapine, Kibana, etc. Take ownership of data analysis tasks pertaining to Video on Demand and Live Streaming products.
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Experience building robust large-scale data pipelines and storage solutions using Python, Pyspark, Databricks, Google/Azure clouds, WCNP, Apache Airflow, and other relevant tools. Data engineering, Data analysis, Data science, Machine Learning, hypotheses & statistical data inference knowledge.
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Seeking a VP with expertise in computational chemistry and data science, ready to enhance and expand a state-of-the-art computational platform. Oversee the growth and management of the computational chemistry and data science teams, fostering a cohesive environment across US and China operations.
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As a member of this team, the Lead Data Integration Engineer will be responsible for DTCC’s data integration and management activities using the Informatica Data Quality & Data Integration Hub and supporting other technologies such as Talend tool suite.
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Experience - Minimum 5 years of experience with proven working experience as a data engineer or data analyst. Sr. Technology Risk Data Engineer - Financials. Cultivate a deep understanding of the Bank's data and risk modeling.
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In our Data science track we prepare you to get job as one of the following: Python developer, a data analyst, data visualization developer, a statistician, a machine learning engineer or a data scientist.
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