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Driving Results: Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using Python and/or Node.js, SQL (using dbt for transformation), commercial SaaS and OSS offerings, and AWS ‘big data’ technologies.
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Moreover, this embedding with DSI nurtures fertile Research and Development collaborations that leverage DSI’s excellence in quantitative pharmacology, biostatistics, and big data. Develop and execute strategies that maximize the value of data science applications (e.g. in AI/ML, Computational Chemistry, and Biology.
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With a bridging seat on the leadership team of DSI, the Head of Computational Science & Data Strategy, Research, ensures a seamless connection with the data infrastructure and governance responsibilities of DSI and its responsibility for enterprise data solutions in partnership with Takeda’s corporate Data & Technology function.
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Experience with at least 3 of the technologies/tools mentioned here: HAProxy, Kafka, Big Data/ Hadoop, Presto, Spark, Airflow, Pinot, Druid, Opensearch, Gcp, Data Proc. Experience scaling production systems running Big Data tools like Spark, Hadoop, Apache Druid, Looker.
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Advances in big chemical data, massive computing power, artificial intelligence, and molecular dynamics simulation are changing the way we develop new drugs. At 1910 Genetics, we put computation at the heart of drug discovery, blending expertise in computational chemistry, structural biology, pharmacology, genetics, data science, and software engineering to develop drugs for previously undruggable targets.
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Job Schedulers – Airflow, Oozie or other ETL Scheduler Data Analytics – AWS Cloud Native Services Big data architecture – Python, Scala, Hadoop Bigdata, Hive, Pyspark etc. Data bases (SQL, NoSQL) and Data Platforms (Databricks, Snowflake) Strong skills with Database management, Data modeling and design DevOps and Security CI/CD, Serverless compute, OAuth, SSO, MFA etc.
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Hands-on experience in LLM training, fine tuning, and/or deployment a big plus. and CUDA a big plus. Solid skills of algorithms, data structure, system architecture, and software engineering.
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Big Data & Streaming: 2+ years using big data and/or streaming technologies (e.g. Apache Spark, Apache Kafka, Apache Flink) Data: Good understanding of data & data processing tools (e.g. Spark, Kafka, SQL), of relational database technologies and of analytics databases (e.g. Redshift, Vertica, Snowflake.
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Familiarity with big data processing frameworks such as Apache Spark and experience with programming languages like Python, Scala, or SQL. Continuously evaluate new cloud technologies and data processing frameworks to enhance the feature store's capabilities, including integration with Azure-based services like Azure Data Factory, Azure Databricks, and others.
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Deliver datasets from onboarding through mapping and automated DQ so they can be handed off to the Data Operations team for DQ maintenance and consumed by research teams and production. The Asset Management Quant Research Engineering team is looking for a Director of Data Engineering who wishes to take on the challenge of building a world-class data operation.
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Help implement data governance policies wherever the data is accessed (Databricks, Snowflake, Spotfire). Azure Data Factory, Databricks (PySpark) and Snowflake preferred. Familiarity with both batch and streaming data operations.
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This entails designing and implementing a sophisticated data governance framework spanning across capabilities and tools such as data quality checks, defining/tracking KPI’s, data lineage and data structure and organization (e.g. organized data hierarchies, well-maintained data dictionary and report catalogue, as well as data and report certification.
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Experience using big data technologies and distributed computing engines, e.g. HDFS, Spark, Kafka, Cassandra, Solr, Dask. Translate business objectives into data and analytics solutions and, translate results into business insights using appropriate data engineering and data science applications.
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Develop/ Build and enhance data pipelines using Python, Spark based data engineering solutions (Databricks), and SQL (AWS Redshift). Hands on experience working in cloud data platforms such as AWS Redshift, Spark based data engineering solutions.
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We are looking to speak to Big Data Architects or developers, or in fact, anyone who has legitimately been through 1 full life cycle using any Cloudera product to drive Big Data Strategies in an organization.
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big data jobs Company: Home Depot in Boston, MA
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