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Prior experience supporting Data Governance initiatives desired: Data Quality, Metadata Management (Data Cataloging, Data Lineage), Master Data Management, Data Security.
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Strong skills in languages such as Python, R, SQL or Scala, alongside experience of using modern and traditional data technologies including: ElasticSearch, MongoDB, PostgreSQL, mySQL/mariaDB, Oracle, SQL Server, Hadoop, Kafka, Splunk/ELK or other logging and monitoring tools, BI and Data Warehousing solutions and ETL and migration technologies.
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Incorporate core data management competencies including data governance, data security and data quality. Build and operationalize complex data solutions, correct problems, apply transformations, and recommend data cleansing/quality solutions.
$105 - $173Full-timeExpandApply NowActive JobUpdated Yesterday - UpvoteDownvoteShare Job
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3+ years of experience in data engineering, data integration, data modeling, data architecture, and ETL/ELT processes to provide quality data and analytics solutions.
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We use Kafka for data pipelines, Apache Beam for ETL, Bitbucket for source control, Jenkins for continuous integration, Grafana + Prometheus for metrics collection, ELK for log shipping and monitoring, Docker for containerisation, Kubernetes for container orchestration, OpenStack for our private cloud, Ansible for architecture automation, and Slack for internal communication.
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Highly proficient working in Azure cloud environment (e.g. Blob / ADLS, Databricks, Azure Data Factory, Event Hub) BS, MS, or PhD in Computer Science, Information Technology, Management Information Systems (MIS), Data Science or related field.
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Utilize the full capabilities of the Databricks platform for data processing, ETL, analytics, and machine learning. Lead and establish robust enterprise-wide data governance practices, ensuring data quality, security, and compliance.
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Well versed with Data consolidation/ ETL, Data Quality, Data Enrichment (D&B), Data Security, Data Governance and Data Harmonization concepts, practices and their implementation.
$82,000 - $109,000 a yearFull-timeExpandApply NowActive JobUpdated Yesterday - UpvoteDownvoteShare Job
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Deep understanding of data engineering principles and best practices, including data modeling, ETL/ELT processes, data warehousing, and data governance. Strong knowledge of cloud platforms (e.g. AWS, Azure, GCP) and associated data services (e.g. Redshift, Data Factory, BigQuery.
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The Digital Health Sr. Data Architect builds high quality data pipelines driving analytic solutions. Design, develop, optimize, and maintain data architecture and pipelines that adhere to ETL principles and business goals; Solve complex data problems to deliver insights that helps our business to achieve their goals; Create data products for analytics and data scientist team members to improve their productivity.
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Hands-on experience with ETL tools, data lakes, and data warehousing solutions (Snowflake, Redshift, BigQuery, etc. Create a federated data management function: Ensure a firm wide partnership and commitment across the functions for data architecture, data engineering, data governance and data quality.
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This role will serve as a hands-on contributor and be a thought leader in the areas of data engineering, cloud data strategy, Business Intelligence, Data Modeling and ETL/ELT.
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Ensure a firm wide partnership and commitment across the functions for data architecture, data engineering, data governance and data quality. Establish and act as ultimate owner of enterprise data quality standards and data dictionary and be accountable, in liaison with the data owners, for data integrity at each step of the life cycle of the data production, aggregation / transformation, controls and reporting.
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Must have: Proficient in SQL and database technologies (e.g., Oracle, SQL Server, PostgreSQL) with hands-on experience in ETL processes and data integration tools. Role : Data Governance + Data Quality.
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Firm understanding of data modeling, master data management, enterprise data warehousing, ETL, reporting, querying, requirements analysis, and data integration techniques.
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