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Experience in developing an end-to-end AWS native platform for building Data lakes (S3, Glue (Crawlers, ETL, Catalog), IAM, CodePipeline, CodeCommit, CloudFormation/Terraform, CloudTrail, CloudWatch, AWS Config, Guard Duty, Secrets Manager, KMS, EC2, Data Visualization Tool: Tableau run on an EC2 or AWS Quicksight), Athena, AWS AI (Bedrock, SageMaker, Q.
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The Federal Reserve Bank of San Francisco is looking for a Sr Cloud Data Engineer to join the Data and Analytics Service (DAS) Team. We are a team based out of San Francisco that partners with business lines across the Federal Reserve System to deliver the cloud + big data engineering, advanced analytics products, and solutions.
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CI/CD orchestration (Jenkins, CircleCI, or TravisCI)ETL, data modeling, cloud-based data storage, processingGCP Data Services (Dataflow, BigQuery, Dataproc)Workflow and data pipeline orchestration (Airflow, Oozie, Jenkins, etc)Container orchestration technologies (Kubernetes, OpenStack, Docker swarm, etc)Open source software involvementMonitoring/Logging with technologies like Splunk, ElasticSearch, Logstash/Fluentd, Stackdriver, Time-series databases like InfluxDB etc.
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About The Role & TeamWe are seeking a Sr. Data Engineer to join our Data Engineering team within our Enterprise Data Insights organization to build data solutions, design and implement ETL/ELT processes and manage our data platform to enable our cross functional stakeholders.
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Work with AWS big data technologies including EMR, Glue, S3, EKS, Lambda, Athena, RDS. 6+ years’ experience working within the AWS Big Data/Hadoop Ecosystem (EMR, Glue and Athena.
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Our services span data governance, data quality strategies, data management architecture, master data management strategy and implementation, analytics and big data.
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Exposure to big data platforms – Snowflake, Redshift, Azure, Matillion, Alteryx, etc. Partner with cross functional teams such as data science, data engineering, product analysts, experimentation and instrumentation teams to contribute to advanced models (MMM, MTA), data pipelines and instrumentation, and build testing roadmap.
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Ability to write and tune complex SQL queriesExpert coding skills in any one of Python, Java, Scala, GolangExperience in data modeling and creating data pipeline ETL jobsExpertise in Linux administrationGood knowledge of basic networking technology and concepts: TCP/UDP, SSL, HTTP, NATBONUS POINTSExperience with ETL tools like Talend, Informatica, Pentaho, etc.
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As a Senior Software Engineer, you will: Design and implement big data, distributed, blockchain, and machine learning systems using modern tech stacks in Golang and Python. Experience with real-time and batch big data sets and platforms, ideally 10-100+ TB.
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Familiarity with “modern data stack” tools and technologies, such as Databricks, Airflow, DBT, and Reverse ETL. You will interface between data engineering and end users to help improve our data tooling and development velocity.
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The trials also involve real world evidence generation, patient reported outcomes (PROs) and analysis of complex big data sets. Minimum Qualifications Master’s degree in Data Science, Mathematics, Statistics, Biostatistics, Bioinformatics, Biological Sciences, or related discipline.
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Specific knowledge of platforms and technologies including SaaS (such as Salesforce.com, ServiceNow), Cloud Hosting (Azure, AWS), ERP Platforms (such as Oracle), Integration and integration frameworks, Business Intelligence & Analytics, Big Data, Reporting and Visualization, Document & Contract Lifecycle Management, Energy Systems and Energy Operations, Construction and Project Management Platforms, RPA, Gen AI, etc.
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The Cyber Security Specialist (Data Security) continuously identifies, tracks, shares, and supports operational IT security requirements across the Ninth Circuit, with an emphasis on data security and analysis.
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We are looking for a Principal Software Architect with experience in data-centric Internet systems, machine learning, cognitive science, AI, monolithic and microservices architectures, modern data architectures, reliability, resiliency, availability, cost efficiency, data privacy, security, compliance, technology selection, mentoring, and structured and unstructured engagements.
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Own and evolve the core of the data pipeline and lifecycle at Directly, whether that involves growing the data model and schema, or implementing systems for data quality and furthering data management through ETL, or improving the efficiency of data ingest.
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