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Advanced proficiency with data, analytics, and visualization technologies (we use SQL, Looker, and Python). As a Data Scientist on the Square Loans team, you will partner with cross functional stakeholders like Product, Credit Policy, Engineering, and Operations to shape and execute strategies that expand credit eligibility, enhance loan gross profit, and improve cohesive banking user experience on a global scale.
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Develop and maintain creative solutions to evolving challenges that span data processing, data modeling, and machine learning model deployment. Partner with the analytics and data science team members to understand and optimize downstream data usage.
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Our products are trusted by leading companies including PayPal, Visa, FTX, Uniswap, Anchorage, and federal agencies such as the FBI and IRS. Every day, we tackle challenges in data engineering, data science, and threat intelligence to advance our mission to build a safer financial system for billions of people.
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As a data engineer, you have experience spanning traditional DW and ETL architectures and big data ecosystems like Databricks / Snowflake / EMR / Airflow. Role DescriptionDropbox is looking for a Data Engineer to join our Finance Data Engineering (FDE) team to build out next generation Financial data pipelines that power crucial business decisions throughout the organization including metrics (ARR, Churn) that are reported externally to Wall St. The team is responsible for curating Gold Datasets (user, team, product etc) that are the source of truth for analytics and operations at DropboxDo you dream about Data and talk to your friends in SQL.
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Experience with data warehousing (e.g., Databricks, Snowflake, Redshift, BigQuery, or similar) Remote Data Analyst. Experience working in Databricks (including delta live tables, data lakehouse patterns, etc.
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Data Quality Systems & Strategy (~30%) Rebuild and streamline data warehouse SQL models that support CALPADS. This is alongside the team-wide ongoing data warehouse migration process to move our infrastructure and functionality to an updated and best in class solution with BigQuery and dbtCloud.
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Bonus points if you have High familiarity with data platforms and applications like Databricks, Jupyter, Snowflake, Airflow, FiveTran, DBT.Previous experience in Sales and GTM analytics, showcasing a strong business sense.
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Experience with data warehouse solutions (like BigQuery/ Redshift/ Snowflake) Experience with data visualization tools and infrastructures (like Tableau/SiSense/Looker/other) As a Senior Data Engineer, you'll have a key role in building and designing the strategy of our finance analytics engineering team under the Enterprise Data Engineering group.
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The OpportunityThe Senior Data Engineer will be responsible for the development, management, and delivery of a robust data engineering and architecture, to build Extraction, Transformation and Load (ETL) Data Pipelines, Data Integration, Data Governance (Data Quality, Data Security) for Genentech.
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Job Requirements: PhD degree, or foreign equivalent, in Electrical Engineering, Computer Science, Statistics or Bioengineering or a related field plus 3 years as a Data Scientist or any job title/occupation/position involving Data Sciences.
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Google Cloud Platform (Data Proc, BigQuery) Data storage and analysis toolsets, including modern distributed technologies such as SparkSQL, Hadoop, Presto, Hive, Kafka, Cassandra, Redshift or other equivalent solutions.
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Proactive and enjoys the data discovery process, digging deep and getting hands-on with data. Conduct in-depth data investigations across the ConcertAI data pipeline to maintain a high level of data quality, completeness, and usability of ConcertAI data products.
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Proficiency in a variety of technologies such as SQL, Bash, Python, Java, Presto, Spark, AWS, DataOps, DevSecOps, data streaming like Kafka, RabbitMQ, data stacks like Airflow, Databricks, dbt, and data stores like Snowflake, Cassandra, Aurora.
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Comfortability with data warehouses and big data technologies such as Redshift, Snowflake, Big Query, Presto, Athena, Spark, DBT. The ideal candidate will be adept in navigating the data stack and able to support initiatives in all facets from analytics/data engineering and product analytics to machine learning.
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Develop, deploy and maintain data processing pipelines using cloud technology such as AWS, Kubernetes, Lambda, Kafka, Airflow, Redshift, S3, Glue, and EMR. 10+ years of experience in data engineering on distributed frameworks such as Spark, Databricks, Kafka Streams, Hadoop etc.
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data job Title: administrative assistant Company: Advance Ems in San Francisco, CA
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