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Our stack is currently built with Segment, Fivetran, Snowflake, Looker, Airflow,Singer. Build ETL-ELT packages usingSinger.io's opened source framework (Python) along with job scheduling (Airflow and others.
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Our data engineering stack run on Python, MySQL, Spark, and Airflow. Our web applications are built primarily using Ruby, Rails, Javascript (Vue.js), and a bit of Golang. You have a deep understanding of object-oriented programming language like Ruby, a web framework like Rails and a modern JavaScript framework like Vue.js.
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Working knowledge of data platform technologies such as: AWS Services (RDS, ECS/EKS, Lambda, S3), ETL Tools (DBT, Airflow), Data Warehouses (Snowflake, Databricks). Working knowledge of data platform technologies such as: AWS Services (RDS, ECS/EKS, Lambda, S3), ETL Tools (DBT, Airflow), Data Warehouses (Snowflake, Databricks.
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Experience with Docker, Kubernetes, Microservices Architecture, Hadoop, Spark, Databricks, Airflow, NiFi, Lucene, Solr, OpenSearch. Experience with Docker, Kubernetes, Microservices Architecture, Hadoop, Spark, Databricks, Airflow, NiFi, Lucene, Solr, OpenSearch.
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Familiar with Spark, MLLib, Databricks MLFlow, Apache Airflow and similar related technologies. Familiar with Spark, MLLib, Databricks MLFlow, Apache Airflow and similar related technologies. Good foundation in Machine Learning (ML), Deep Learning, Large Language Models (LLM) and Natural Language Processing (NLP.
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Experience working with Airflow as workflow management tools and build operators to connect, extract and ingest data as needed. Keywords : GCP, Google Cloud Platform, BigQuery, Composer, SQL, Python, Data Engineering, Data Pipelines.
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3+ years' of experience with workflow management engines (i.e. Airflow, Luigi, Prefect, Dagster, digdag.io, Google Cloud Composer, AWS Step Functions, Azure Data Factory, UC4, Control-M). Experience working with cloud or on-prem Big Data/MPP analytics platform (i.e. SnowFlake, Netezza, Teradata, AWS Redshift, Google BigQuery, Azure Data Warehouse, or similar.
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Background in Data Engineering, 5+ years of experience building and maintaining scalable data infrastructure, including distributed processing solutions (e.g. Spark), cloud-based data lakes and warehouses (e.g. Databricks, Snowflake, BigQuery), workflow management (e.g. Airflow, Luigi), and data transformation tools (e.g. DBT.
$159,400 - $210,900 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Nice to have] Experience working both real-time systems like Airflow Kafka, DBT, and batch data pipelines in Redshift, Presto, and Data Lakes. We use tools like DBT, Airflow, Redshift, ElasticSearch, Atlan, and Retool to orchestrate data pipelines and define workflows.
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Experience with Apache Airflow and Apache Spark; Spark performance turning. Experience with Healthcare data, e.g., clinical trial data, electronic medical records, and insurance claims; or Bioscience’s data, e.g., protein or small molecule data, or bioinformatics; or Biopharmaceutical manufacturing.
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Proficiency in Apache Airflow, ECS/EKS,Kafka, Docker, Terraform, and disaster recovery strategies. Oversee the implementation and use ofdata storage solutions, including PostgreSQL, MS SQL, Amazon RDS/Aurora,Databricks, and Snowflake.
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Extensive experience with scientific libraries in Python (numba, pandas) and machine learning tools and frameworks (scikit-learn, tensorflow, torch, etc. Qualifications: Strong foundation in Machine Learning (ML), Deep Learning, LLMs and NLP.
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We use proprietary and open source technologies, Kafka, Spark, Iceberg, Airflow, Presto, etc. We use proprietary and open source technologies, Kafka, Spark, Iceberg, Airflow, Presto, etc. Proficiency in at-least one of the following programming languages - Python, Scala or Java. Experience in multi-threaded, concurrent programming and synchronization Cloud technology experience on platforms like AWS, Microsoft Azure, Google Cloud Experience developing Big Data applications using java, Spark, Kafka is a huge plus.
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Includes Snowflake, Apache Airflow, AWS cloud infrastructure (e.g., Kinesis, Kubernetes/EKS, Lambda, Aurora RDS PostgreSQL), Python and Typescript. Experience building and managing production-grade data pipelines using tools such as Airflow, dbt, DataHub, MLFlow.
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Building a modern datastack, using tools such as dbt, Dagster, Airflow, Snowflake, et cetera. Building a modern datastack, using tools such as dbt, Dagster, Airflow, Snowflake, et cetera. We offer holistic total rewards, including comprehensive healthcare plans, family building benefits (fertility and adoption/surrogacy support), flexible time off, free OneMedical memberships for you and your dependents, and a 401(k) retirement plan with employer match.
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