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Proficiency in Python, PySpark Scala, Java, SQL, Airflow, dbt. Programming languages: 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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Net, C#, Qlik, Power BI, Machine Learning, Azure Data Factory, RedShift, UiPath, Cloud, RPA, AWS, Redshift, Kinesis, QuickSight, SageMaker, S3, Databricks, AWS Lake Formation, Snowflake, Python, Qlik, Athena, Data Pipeline, Glue, Star Schema, Data Modeling, Performance Tuning, SQL Individual salaries that are offered to a candidate are determined after consideration of numerous factors including but not limited to the candidate’s qualifications, experience, skills, and geography.
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Experience with DB technologies (e.g., SQL, Python, PostgreSQL, AWS Aurora, AWS RDS, MongoDB, Redis). Be an expert in cloud data warehouse, cloud ETL tools and capabilities to engineer solutions and automate large scale data flows from varying systems/sources.
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Expertise in ETL/ELT, SQL, and cloud data integration tools (Azure Data Factory, AWS Glue, Informatica, etc. In this dynamic role as a Senior Data Engineer, youll be at the forefront of shaping and implementing cutting-edge data solutions.
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Skills :- Python, SQL, PowerBI, Snowflake, Foundry (Palantir), Plotly, MS Azure data bricks, and understand data acquisition, signal processing, NVH and Diagnostics CAN. Statistics, Data Science/Analytics, Management Information Systems, Mathematics.
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Proven experience in SQL, data modeling, data warehousing, and data analysis. Skilled in modern data platforms (Azure Synapse, Databricks, Redshift, Big Query, Snowflake.
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NET, Python, and Amazon Web Services; load data into reporting applications that can be accessed by clients; apply automation and testing processes to enhancements as data is added to the Reporting Database; and guide and mentor junior-level engineers.
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Nice-to-have) Experience with Python, Golang, SQL, and/or AWS. By offering products through our CRA, we can deliver a holistic view of a user’s credit profile by composing various data streams with deep data insights that Plaid can uniquely offer.
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Experience in master data (MDM), metadata, and data governance tools (Alation, Erwin, Collibra, Azure Purview). Mentorship and Leadership: Guide and support data engineering teams, enhancing business data processes and systems.
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Policy Implementation: Apply metadata management and master data strategies within data workflows. Skill Development: Assist in developing a training plan for advanced data engineering skills.
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Databricks, Python, JavaScript, Kafka, dbt, Terraform, Snowflake, SQL, Jenkins, Github, Airflow, OpenSearch (Elasticsearch & Kibana), Talend, Alation, Neo4j, Hashicorp Vault / AWS Secrets Manager, Docker / OpenShift / Open Cloud Foundry, MongoDB, Docker, BlackDuck, SonarQube.
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Design, develop, and maintain scalable and efficient data pipelines for extracting, transforming, and loading (ETL) data from various sources into our data warehouse leveraging AWS, Databricks, Python and SQL technologies.
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At least 5 years of data engineering experience. Prototype Development: Create and implement prototypes and proofs of concept, integrating your deep understanding of data workflow design.
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At least 6+ years of data engineering experience including data management, analysis, design and integration/ETL. Prior experience of Insurance domain a huge plus Strong knowledge of MDM technology, SQL and advanced programming languages such as Python/Java Hands-on experience with MDM tools and Cloud services.
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Hands on Technical experience with various Data Product development tools, utilities, and frameworks such as Azure Delta Lake/ Synapse, Snowflake, Databrick, IBM DataStage, Informatica, Oracle PL/SQL, and Master Data concepts.
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