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What you will be responsible forDevelop/ Build and enhance data pipelines using Python, Spark based data engineering solutions (Databricks), and SQL (AWS Redshift). Hands on development in Python, PL/SQL, SQL, Shell Scripting, AutoSys. Hands on experience working in cloud data platforms such as AWS Redshift, Spark based data engineering solutions.
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Exp designing and delivering data engineering solutions with Databricks Exp in Azure Databricks Experience in Data Engineer in zure Big Data Environment Expertise in ETL tools i.e. (Infomatica, Talend, SSIS, Data Stage) Expertise in Implementing Data Warehousing Solutions Exp with working in Agile (KANBAN, Scrumban) environment Exp in zure stack ( zure Data Lake, zure Data Factory) Exp in Scala or Python, T-SQL.
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The technology stack of this CSR data product build is Azure SQL Datawarehouse, Python, Databricks, and Azure Datalake at a high level. As a member of the data engineering team, you will help lead the development of very large and complex data applications into public cloud environments directly impacting the design, architecture, and implementation of this client's flagship data products around topics like revenue management, supply chain, manufacturing, and logistics.
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Knowledge of Hyperscale SQL Server and Snowflake Data engineering best practices around data modelling, evaluation, deployment, and governance. Expertise in building robust data engineering frameworks, Azure cloud, python/JAVA/Scala, and Data bricks (Apache Spark.
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Advanced experience in SQL in big data warehouse systems such as Snowflake, BigQuery, Databricks, etc. What you’ll be responsible for:As a member of the Data Engineering - Business ETL team, you own the ETL/ELT pipelines and data warehouse that is used for financial and regulatory reporting.
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Minimum RequirementsBachelor's degree in business administration, computer science, mathematics/statistics, engineering, management information science, data processing or related field and experience with Tableau, Cognos/Argos, SQL, and/or SAS is required.
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As a member of the Data Engineering - Business ETL team, you own the data warehouse and data pipelines that are used for financial and regulatory reporting. 4+ years of professional data engineering experience.
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Applicants should also have a demonstrated understanding and experience using software and tools including big data tools like Kafka, Spark; relational SQL databases including Big Query or equivalent; workflow management and pipeline tools such as Airflow, and object function/object-oriented scripting languages including Scala, C.
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Understanding of enterprise data management concepts (Data Governance, Data Engineering, Data Science, Data Lake, Data Warehouse, Data Sharing, Data Applications.
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Design and implement cloud-based solutions using Microsoft Fabric technology stack and Microsoft SQL Server, including Azure Synapse Data Engineering, Azure Data Factory, Synapse Data Science, Synapse Data Warehouse, Synapse Real-Time Analytics, Onelake, and Power BI, with a focus on data migration, movement, and transformation.
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Our engineering team at OpenSea is in search of a strong, driving, but curiousData Engineer to take charge of our data replication, ETL, and warehousing infrastructure to empower our core user experiences, trust & safety models, and business intelligence use cases.
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As a member of the Data Engineering Blockchain team, you own the ETL/ELT pipelines and data warehouse that are used to analyze trends and activity on the blockchain. Working closely across groups, such as the product, engineering, data science, and external partners for data modeling, general management of data life cycle, data governance and processes for meeting regulatory and legal requirements.
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Experience / Familiarity with Data Science techniques a plusExperience / Familiarity with Data Engineering techniques a plus. Technical Qualifications: Programming Experience using Visual Studio VB.Net, C#, ASP.Net, Oracle PL\SQL, JSON, REST APIs and Crystal ReportsSQL Server Integration Services (SSIS) and SQL Server Reporting Services (SSRS) experience a plus.
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Technical Proficiency: Proficient in SQL, Excel, and data visualization techniques. Be familiar with our current data warehouse and data models, know where to find business critical data and provide requirements to the product and data engineering teams.
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At least two years experience in implementing, automating and integrating Big Data infrastructure resources like S3, Redshift, Aurora, Kinesis, Kafka, EMR, Lambda, SNS, Azure Blob Storage Account ,SQL Data Warehouse, Microsoft Event Hubs , HDInsights, Azure Databricks, Azure Functions,Event Grid, Data Lake Analytics in an ephemeral/transient and in an elastic manner.
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