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Their tenant is a strong cross-domain team to deliver E2E solutions covering tech areas ranging from machine learning, big data, microservices to data visualization. 3+ years of big data development experience with technical stacks like Spark, Flink, Singlestore, Kafka, Nifi and AWS big data technologies.
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Experience in handling Big Data, IOT Data, and specifically a variety of Big Data file Formats Avro, Parquet, etc. Exposure to Data Lake, Lake House concepts, and technologies like Snowflake, AWS RedShift, or Databricks.
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Experience in Data Engineering tools such as Talend, SAP BODS, Python, PySpark, AWS S3, Glue, Athena, EMR, Kafka or similar tools. Management of strategies, goals, objectives, metrics, KPIs/KPMs, and optionally, enterprise scorecards Support GRC (Governance, Risk, Compliance) aspects of data management.
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At least 5-7 years of experience with managing and transforming big data in excess of 1 billion rows, data analytics and insights within the music industry, preferably using AI / ML modeling data analysis tools and techniques to drive significant change in an organization.
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Big Data Technologies: Hadoop, Spark, Athena, materialized views. Data Visualization: Tableau, Looker, Power BI, Quicksight. Highly experienced Full stack Engineer with expertise in cloud engineering, data engineering, and data warehousing, particularly within the AWS and google cloud ecosystem.
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SKill: Big Data Architect, Cloudera Big Data Tool stack (Hive ,Impala, OoZie), Pyspark, Teradata, Shell Script, Unix programing, Hadoop 8+ years of data engineering/architecture experience in Waterfall and Agile Methodology, with a proven track record of delivering complex data solutions, preferably in the financial sector.
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Develop and optimize Databricks notebooks for big data processing and analytics in Azure Databricks environments. Expertise in SQL Server Management, Azure Data Factory Development, Databricks Engineering, Delta Parquet File Management, and SQL Server Integration Services.
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Develop and maintain scalable, reliable, and cost-effective data solutions using AWS technologies and big data tools like Databricks, Airflow, and Dremio. Proven experience as a Data Engineer, Data Reliability Engineer, or in a similar role in an SRE environment.
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Experience with big data frameworks and tools such as Spark, Hadoop, Kafka, and Hive. Expert in SQL, with knowledge of efficient query and schema design, DDL, data modeling, and stored procedures.
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Utilize advanced analytics tools and platforms (e.g., SQL, Python, R, Tableau, BigQuery, AWS Bedrock, etc) for data processing and visualization at scale. Expert proficiency in SQL based data manipulation, analysis tools (e.g., Python, R), ETL processes, and data visualization platforms (e.g., Tableau, Power BI, BigQuery.
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Active Google Cloud Data Engineer Certification Hands on in designing, building and operationalizing large-scale enterprise data solutions and applications using one or more of GCP data and analytics services in combination with 3rd parties - Python, Pyspark, Spark, Hive, Cloud DataProc, Cloud Dataflow, Apache Beam/ composer, Big Table, Cloud BigQuery, Cloud PubSub, Cloud storage Cloud Functions & GitHub.
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Architect and implement big data processing solutions using Databricks, leveraging Apache Spark for data transformation, ETL processes, and machine learning workflows. Proficiency in Databricks, Apache Spark, and related big data technologies.
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Experience in big data technologies like Spark/Hive, Kafka, Apache Parquet, Avro, GCS, GCP, BigQuery. Data engineering, database engineering, business intelligence, or business analytics, Master's degree in Computer Science or related field and 2 years' experience in software engineering or related field, We value candidates with a background in creating inclusive digital experiences, demonstrating knowledge in implementing Web Content Accessibility Guidelines (WCAG) 2.2 AA standards, assistive technologies, and integrating digital accessibility seamlessly.
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Experience with big data technologies (Spark, Clickhouse, Redshift, Snowflake etc.) Define data engineering standards and best practices and drive operational excellence. Proven success in communication with database users, software engineers and senior management to collect requirements, discuss data modeling decisions and define data engineering strategies.
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Experience with data modeling, data warehousing, and ETL pipeline development. Partner with data scientists and machine learning engineers to evolve, optimize, and integrate critical predictive models.
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big data jobs Company: Uniti Med in MD, Ohio
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