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The ideal candidate will lead a team responsible for designing, implementing, and maintaining data solutions using SQL, Databricks, Postgres, and Redshift. Minimum of 10 years of experience in data engineering roles and 2+ years leading / maintaining data DevOps for Data Pipeline and ETL solutions with a focus on SQL, Databricks, Postgres, and Redshift.
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We’re looking to bring on a Demand Gen Lead to help level up our pipeline generation programs. Your current role might be Demand Generation, Business Development, Growth Marketing – or somewhere in between.
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This is a critical role that will provide thought leadership in an enterprise-wide data and digital transformation imitative and devise clear roadmaps to deliver next generation data solutions.
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6 years of experience must include: Ab initio, Informatica, Data Stage; Teradata, IBM DB2, Cognos, Oracle PL SQL; Autosys, Control-M, Erwin, XML, HTML, CSS, Unix Shell Scripts; Data Analysis, Data Processing, Code Optimization, Performance tuning; Automating Business Process and Models; Microsoft Visio, Web Services, Crystal Reports; and HP Quality Center, ALM, VSS, EME, XSD. At least 3 years must include: Hadoop, HDFS, Big Data, Hive, Spark.
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The ideal candidate has 2+ years working with SQL and data visualization tools to query large data-sets (e.g. Presto/Trino, Spark SQL, Tableau, etc. 2+ years working with SQL and data visualization tools to query large data-sets (e.g. Presto/Trino, Spark SQL, Tableau, etc.
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Lead Software Engineer, Full Stack (Enterprise Data) Enterprise Data is hiring a Lead Software Engineer! Python, Apache Airflow, Apache Spark, PostgreSQL, Presto SQL, Snowflake SQL.
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Assist cyber analysts in solving complex big data search processing challenges using expert SPL knowledge. At least 3 years of hands-on Splunk Administration experience in a multi-indexer search head cluster environment.
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Investigate and repair application defects regardless of component: RPA, vendor integration, front-end, business logic, middleware, or database (PL/SQL and Data Modeling). Experience with UiPath Studio and associated tools such as Recording, Screen Scraping, Data Scraping, Ui Explorer, Package Management, Debug, and Workflow Analyzer.
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Data bases (SQL, NoSQL) and Data Platforms (Databricks, Snowflake) Strong skills with Database management, Data modeling and design DevOps and Security CI/CD, Serverless compute, OAuth, SSO, MFA etc.
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The Lead QA Analyst is responsible for testing BI Data solutions and enterprise semantic model, while coordinating the development of these requirements with the Data engineering team.
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Primary focus on Java application development and enhancements using Java, Python, SQL, Snowflake DB, Databricks spark/Notebook, Azure Blob storage. Production support to the data analysis platform including the Java application and Tidal scheduler.
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Utilize programming languages like Scala, Java, Python, SQL and GO and NoSQL databases and Cloud based data warehousing services such as Redshift and Snowflake. As a Capital One Senior Lead Data Engineer, you’ll have the opportunity to be on the forefront of driving a major transformation within Capital One.
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Technologies/Tools we use: Python, PySpark, SQL, Azure Promptflow, Azure Search, Cognitive Services, R Studio, Databricks, Azure ADO, Azure AI/ML Data Factory, Azure (Virtual Machines, Azure Web Apps, Cloud Storage, Azure ML), Anaconda packages, Git, GitHub, GitHub Actions, Terraform, Artifactory, Airflow, Docker, Kubernetes, Linux/Windows VMs, Dynatrace, Slack for monitoring and alerting.
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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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As a Senior Lead Engineer in Finance tech you will be responsible for working collaboratively across Finance, Capital One Lines of Business and Enterprise Functions to develop patterns, playbooks and tools to support standards, create and review target data models in support of Finance and Enterprise goals.
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