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Job Title: ETL Data Engineer (ONSITE) Location: Washington, DC Duration: 12 Months+ Job Description: The Enterprise Data team at client requires an ETL data engineer to support data operations for its Cloud Data Exchange.
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INTERCOLLEGIATE ATHLETICS SCOREBOARD OPERATIONS Responsible for all computer related audio equipment and network devices for all NCAA Division 1 Athletic contests and associated Athletic department events requiring timing and scoring.
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Hands-on experience with data warehousing, data lakes, and analytics platforms (e.g., Snowflake, Redshift, BigQuery). · Familiarity with data security principles and practices· Experience with data visualization tools (e.g., Tableau, Power BI) and statistical analysis tools (e.g., R, Python.
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Oversees the activities of OT, SCADA, and operations system support teams; includes Sound Transit data centers, network infrastructure, database administration, client systems, and operational field systems.
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Must have proficient knowledge with three or more of the following technologies: Application / stateful / UTM firewalls; SIEM; DLP; Web content filtering; Web application firewalls (WAF); Vulnerability scanning and penetration testing; IPS/IDS; Security Operations Center operations; Wireless Networking; UNIX, AIX & Solaris, Linux, Windows Server Operating Systems; Endpoint and Malware.
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Qualified candidate should have a strong knowledge of current sports ticket sales and operations. The Regional Director of Ticketing Operations will directly support all NASCAR Consumer Strategy & Ticket Operations initiatives.
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Experience with cloud computing platforms (, AWS, Azure, GCP) and cloud native computingProficiency in programming languages like Python, Java, Scala, C#, SQLExpertise in big data technologies such as Hadoop, Spark, Kafka, and NoSQL databasesKnowledge of data modeling, ETL processes, and data warehousing conceptsExceptional problem-solving abilities and teamwork skills.
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Check out our locations, benefits, and more at Job DescriptionThe Data Science team at Cash App derives valuable insights from our extremely unique datasets and turns those insights into actions that improve the experience for our customers every day.
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Work closely with product, engineering, credit, and operations teams to ensure data science models are aligned with overall business strategies. Expertise in programming languages such as Python, R, and SQL, and experience with big data technologies like Databricks and Spark.
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Solid understanding of various Data Management and Data Pipeline tools available in AWS.Working knowledge with primary AWS Services like S3, Lambda, Batch, Glue, Athena, EC2, EBS, CloudWatch, CloudTrail, ECS, ECR, EMR, IAM, SNS etc.
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You have a strong knowledge and practical experiences with high density data types in a clinical setting (genomics, transcriptomics, proteomics, flow cytometry, TCR sequencing, immune assays, etc.
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Experience working with cloud or on-prem Big Data/MPP analytics platform (i.e. SnowFlake,Netezza, Teradata, AWS Redshift, Google BigQuery, Azure Data Warehouse, or similar). 3+ years' of experience with workflow management engines (i.e. Airflow, Luigi, Prefect, Dagster, digdag.io, Google Cloud Composer, AWS Step Functions, Azure Data Factory, UC4, Control-M.
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You have good knowledge and understanding of data science approaches including machine and deep learning; application in imaging domain (H&Es, IHC, radiomics) a plus. For more than 20 years, its passionate, innovative and collaborative team has invented next-generation antibody technology platforms and leveraged translational research and data sciences, which has resulted in a proprietary pipeline including bispecific T-cell engagers, next-generation immune checkpoint modulators, effector function enhanced antibodies and antibody-drug conjugates.
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Utilize programming languages like SQL, Python and tools like databricks, iceberg, presto, graphql and Cloud based data technologies. The Senior Lead Data will be responsible for partnering with Technology, Machine Learning, Product Architecture, and other Capital One teams to support the development of the Finance Tech Data Strategy into products and services we create and consume.
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Understand how ontology and data models fits into the larger ecosystem, anticipating impacts from changes in other parts of the business. Work closely with tech leadership and architecture to understand the tradeoffs of technical decisions and how they impact Ontology and Data modeling as well as consumers.
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Title: data operations Company: Canoe Intelligence
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