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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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What we value Demonstrated experience in Python, PySpark and SQL (AWS Redshift, Postgres, Oracle). As pioneers in index, ETF, and ESG investing, we are always inventing new ways to invest. And, as pioneers in index, ETF, and ESG investing, we are always inventing new ways to invest.
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Utilize programming languages like Java, Scala, Python and Open Source RDBMS and NoSQL databases and Cloud based data warehousing services such as Redshift, Snowflake, AWS. 4+ years of data warehousing experience (Redshift, Snowflake, other.
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Familiarity with airflow, redshift, and dbt. Data: RDS, Redshift, ElasticCache, S3. Ability to run and modify Infrastructure as Code (IaC) scripts in CDK, CloudFormation, Terraform, and Ansible.
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Provision, configure, and manage cloud database instances on various platforms, including Snowflake, Redshift, AWS (RDS, Aurora), GCP (Cloud SQL), and Azure (SQL Database, Cosmos DB). In this role, you will be responsible for the administration, maintenance, and optimization of our cloud-based databases on various platforms, including Snowflake, Redshift, AWS, GCP, and Azure.
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Experience with common data languages (e.g., Python, Scala) and data warehouses (e.g., Redshift, BigQuery, Snowflake, Databricks) 1+ Managing a cloud-based data environment that has complex platform integration and orchestration using tools like AWS, Airflow, Talend, RedShift, or similar.
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Hands-on experience with Postgres SQL, RedShift, Redshift spectrum, AWS Glue, Athena, Snowflake utilities, Snow SQL, Snow Pipe, AWS Lambda model techniques using Python. Experience in Data Migration from RDBMS to Redshift/Snowflake cloud data warehouse.
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Knowledge of AWS Services such as Redshift, Glue, S3, Kinesis, Athena, and Redshift Spectrum or equivalent with another cloud provider. Knowledge of AWS Services such as Redshift, Glue, S3, Kinesis, Athena, and Redshift Spectrum or equivalent with another cloud provider.
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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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Implementation experience of two or more of Modern data platforms (AWS/Azure Synapse/Redshift/Snowflake, Databricks, Redshift, Big Query,) required. Utilize programming languages like Java, Pyspark, Python and Open Source RDBMS and NoSQL databases, and Cloud-based data warehousing services such as Redshift and Snowflake.
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Proficiency in Python, PySpark Scala, Java, SQL, Airflow, dbt. Advanced experience with modern data platforms (Azure Synapse, Databricks, etc.) Demonstrated experience with Continuous Integration and Continuous Delivery systems (Azure DevOps, GitHub.
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Architect, implement, and optimize data pipelines using SQL, Databricks, Postgres, and Redshift to ensure efficient data processing and storage. The ideal candidate will lead a team responsible for designing, implementing, and maintaining data solutions using SQL, Databricks, Postgres, and Redshift.
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Demonstrated multi-year experience with AWS tech stack (Glue, S3, Athena, Redshift), Salesforce, Informatica, Snowflake, Tableau and MuleSoft. Experience with Salesforce, AWS Redshift, Snowflake, Informatica, Tableau, MuleSoft.
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Center 3 (19075), United States of America, McLean, VirginiaSenior Manager, Data Engineering - Capital One Software (Remote Eligible) Do you love building and pioneering in the technology space? At least 1 year experience with cloud computing (AWS, Microsoft Azure, Google Cloud.
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At least 3 years of experience managing Cloud Data Warehouse database technologies, such as AWS Redshift, Snowflake, Databricks or similar. Design, configure, and manage Data Warehouse Database Systems on Cloud (AWS Redshift, EMR HBase and Snowflake.
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