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In depth knowledge of modern data technologies including Snowflake, RedShift, Azure SQL/Synaspe, Databricks or similar technologies. Conduct data management maturity assessments and identify pain points for including data quality, governance, architecture, analytics, metadata management, master data management.
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Experience with data pipeline development and management and data warehousing (Redshift, Snowflake) is a must. As the Director of Data Engineering, you will play a critical role in leading and expanding our data engineering competency to ensure the highest quality, integrity, efficiency, and scalability of our data services and infrastructure.
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Assists with Data Quality (DQ) and Master Data Management (MDM) initiatives. Familiarity with Visual Studio, Master Data Management, data quality tools, data integration tools, R, and Python is desired.
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This individual will also be responsible for creating awareness of AHEAD's world class services that integrate with our key partners, AWS, Microsoft, Harness, GitHub, Palo Alto Networks and RedHat, while working closely with current and prospective customers to determine how AHEAD can best serve their app and data modernization initiatives.
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Data processing (Kafka, Snowflake, ETL, Stream Processing, Code quickly with high quality using primarily Rust, Go, Python, and/or Java. As a member of our team, we look first and foremost for people who are energized by solving business problems through high quality innovation and principled engineering practices.
$190,000 - $300,000 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Knowledge of Master Data Management (MDM) business processes or solutions and data governance and data quality methodologies. Prior experience as primary lead of a large-scale program to migrate/onboard on-premises Data ware solution to Azure cloud platform and Snowflake.
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Cloud experience (AWS, Snowflake, GCP, Azure, Adobe) The Senior Client Services Manager also participates in defining requirements for account related projects as well as resolution of client delivery issues and seamless quality assurance of client deliverables.
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We implement the systems that require the highest data throughput in Java. Within Data Engineering we use Dataiku, Snowflake, Prometheus, and ArcticDB heavily. · Data Governance - cataloguing the thousands of datasets at Man ; allowing discovery, permission ing , data quality, and end-to-end data lineage.
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Experience with data governance, data quality, and regulatory compliance is a plus. Knowledge of and ability to utilize statistical, reporting, and database packages (such as or equivalent to Snowflake, Oracle, SPSS, and APEX.
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Architecture - Designs, creates, and maintains the conceptual data infrastructure to support data quality, data stewardship, data integration, data migration and system collaboration in support of business model and information needs.
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Familiarity with Data Lake and Lakehouse technologies such as Redshift, Snowflake, Databricks, AWS Glue, etc. Familiarity with Big data processing such as Hadoop, Kafka, Cassandra, Spark, HBase, Hive, NoSQL, etc.
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Experience with data warehousing platforms (e.g. Snowflake, Redshift, Databricks, etc.) Data Processing : Design a robust data engineering ecosystem for the efficient design, implementation, and processing of data pipelines to handle large volumes of diverse data sources while maintaining lineage, data quality and compliance.
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Extensive hands-on experience implementing data migration and data processing using AWS services: VPC/SG, EC2, S3, Autoscaling, CloudFormation, Lake Formation, DMS, Kinesis, Kafka, Nifi, CDC processing Redshift, Snowflake, RDS, Aurora, Neptune, DynamoDB, CloudTrail, CloudWatch, Docker, Lambda, Spark, Glue, Sage Maker, AI/Client, API GW, etc.
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Knowledge of healthcare with relative experience in Claims data, Risk adjustment, Medicare, Quality (5-Star, QRS, HEDIS), and Medicaid. Experience with Azure DevOps, Azure Data Factory, Azure Data Bricks, SSIS, PowerBI.
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Sound knowledge on Snowflake, Databricks, Informatica and other leading Data Products. Knowledge in enterprise data management architecture and principles, master data management, data governance and data quality.
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