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Deloitte Risk and Financial Advisory's Financial Services Industry (FSI) Risk, Data and Regulatory professionals help organizations identify the regulatory changes impacting their business and implement effective and efficient processes to manage regulatory, capital, liquidity and interest rate risks, respond to evolving regulatory environments and implement programs, measures, and tools to help clients with a specific focus on risk, data, and regulatory processes.
$107,659 - $207,763 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Experience in Data science, machine learning, or optimization models Experience in Python, Spark, Scala, or R, using open source frameworks (for example: scikit learn, TensorFlow, Pytorch) You are knowledgeable of databases, data warehouse design, cloud storage, and ETL best practices.
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3+ years of experience with data applications and tools (Snowflake, Databricks, Spark, EMR, Presto, Datadog, DBT, etc.) To build this topology graph, ROCI consumes data from a number of BSS/OSS, construction, provisioning, planning, and monitoring platforms to create linkages previously unseen across the company.
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Extensive experience working with various data technologies and tools such as Airflow, Snowflake, Meltano, Fivetran, DBT, AWS, and Looker. By establishing HackerOne's one source of truth and developing data products and solutions on this foundation, we help our Hackers, Customers, and Hackeronies gain insights and make better decisions, thereby empowering the world to build a safer internet.
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Be very familiar with databases & data warehouses: Snowflake, Redshift, BigQuery, Postgres, MySQL, etc. Create beautiful dashboards and reports, and work with customers to create self-service exploration data cubes using Looker.
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Uses coding skills to enable multiple sources to move data through Snowflake, MDM, RDS, and other cloud data environments using tools such as Spark, Striim, Kafka, Snowpipe, Azure Data factory, AWS Glue, Matillion, FiveTran and more.
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Create, maintain and monitor data flows in Spark, Hive, SQL and Presto for consistency, accuracy and lag time. Presto - fast parallel data warehouse and data federation layer. Create different consumers for data in Kafka using Spark Streaming for near time aggregation.
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Cloud: AWS or Azure or GCP in Data Engineering and Analytics streams. Familiarity in Data Engineering tools, data ops tools in On-prem and Cloud eco system. Familiarity with one of the Data Governance & Data Catalog tools.
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Strong Experience with cloud-based data warehouses - e.g., Snowflake, Big Query, Synapse, RedShift, etc. Identify data needs for business and product teams, understand their specific requirements for metrics and analysis, and build efficient and scalable data pipelines to enable data-driven decisions across DICKs.
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Profound knowledge of AWS Data services (eg: Redshift, S3, EMR, Glue, RDS, Kinesis, etc.) Participate in the overall strategic direction of the data engineering technology stack. Experience with Data management and Data governance processes and standards.
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Proficiency in data engineering tools such as Databricks, Apache Spark, Delta Lake, MLflow, and SQL. Work with data engineering platforms and tools such as Databricks, Apache Spark, Delta Lake, MLflow, and SQL.
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Experience with linux/unix/bash scripting is preferred; extensive knowledge of tools useful in manipulating and converting data; facility with numbers and meticulous attention to detail; experience with statistics and statistical programming (R preferred); programming experience with one or more general-purpose programming languages preferred; capacity for learning new technical skills independently; experience with neuroimaging tools (SPM, AFNI, FSL) preferred.
$51,824 - $80,000 a yearFull-timeExpandApply NowActive JobUpdated 2 days ago - UpvoteDownvoteShare Job
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Expertise in programming languages such as Python or Java, and experience in data processing frameworks (e.g., Apache Spark, Kafka). Advanced knowledge of SQL, database management systems (e.g., PostgreSQL, Redshift), and data warehousing solutions.
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Proven experience in building solutions with hybrid data architectures such as, but not limited to Airflow, Snaplogic, Informatica, Boomi or MuleSoft. The candidate will have knowledge of modern data architecture, Cloud architectures, and integration patterns such as RDBMS, Snowflake, Azure Event Architecture, Web Services, Machine Learning and Data Virtualization via federation technologies.
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Utilizing Python, Kotlin, Snowflake, DBT, and Kubernetes, you will play a crucial role in enhancing our data infrastructure. Extensive experience with DBT and Snowflake for data warehousing, including complex SQL/ETL/ELT workflows.
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Title: data Company: Deloitte in Philadelphia, Anoka, Minnesota
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