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As a big data engineer at Booz Allen, you’ll implement data engineering activities on some of the most mission-driven projects in the industry. We need an experienced data engineer like you to help our clients find answers in their big data to impact important missions—from fraud detection to cancer research to national intelligence.
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3+ years designing and building large-scale data processing systems using Apache Pyspark, Apache NiFi, and other big data technologies. This role requires extensive experience in Cloud-based EDW platforms and a strong foundation in big data technologies.
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The company utilizes cutting-edge cloud-based technology, including AI, Robotic Process Automation (RPA), IVA and bots, Actionable Analytics and reports leveraging Big Data, and more.
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Experience with GCP products such as BigTable, BigQuery, Dataproc, PubSub or other data tools like Kafka, Hive, or Apache Beam. Project categories: besides product requests and enhancements, we work on projects in performance and cost optimization, security and legal compliance, reliability and quality initiatives, as well as architectural refreshes such as POC in restructuring our data to be more streaming/real time friendly, new REST API for relevant services, migration of apps to Spark, Parquet, and other new technologies that could make our processing faster, more scalable, maintainable and cost effective.
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Plus, to be a Committer or PMC Member with the Apache Software Foundation on big data technologies: Apache Spark, Hadoop Stack, Kafka, Comet, Atlas, delta. Recognized Subject-Matter Expert big data engineering and cloud-based data warehouse.
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Develop data, analytic, and data science models using Python, PySpark, Large Language Models (LLM) algorithms, and working with other big data tools such as AutoML, BigQuery(BQ), BQML, DataPrep and Jupyter Notebook.
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Our 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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Implementation experience of two or more of Modern data platforms (AWS/Azure Synapse/Redshift/Snowflake, Databricks, Redshift, Big Query,) required. Collaborative Design: Lead efforts in developing high-performing data solutions in partnership with various technology teams.
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Build next-gen cloud based big data infrastructure for batch and streaming data applications, and continuously improve performance, scalability and availability. This engineering group focuses on big data infrastructure, operational data, audience solution, inventory forecasting and full funnel measurements as a foundation layer for addressable Ad Platforms.
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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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Collaborate with other peer organizations (e.g., Business Analyst, Data Modeler, QA, technical support, etc.) You understand the challenges of handling data at scale and are always looking for opportunities to leverage open-source tools to accelerate development.
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We are interested in a variety of topics including data visualization and interactivity, applied machine learning and AI, AI bias and trust, big graphs, natural language processing, data mining, big data management, and heterogenous data analytics.
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Familiarity with AWS cloud platform and the associated big data frameworks (Spark, Redis, Bigquery/Redshift, Airflow, Glue, Snowflake, Databricks). As a Data Engineer, you'll play a pivotal role in designing and maintaining our data infrastructure, ensuring the integrity and accessibility of our data for informed decision-making.
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Experience solving Big Data batch and real-time data processing problems, with large-scale analytics and data processing engines such as Apache Spark and Databricks. Knowledge of Python data science tools, such as SciKit, Numpy, TensorFlow, Jupyter.
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Hands-on experience with Databricks for big data processing and analytics. The ideal candidate will lead a team responsible for designing, implementing, and maintaining data solutions using SQL, Databricks, Postgres, and Redshift.
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big data jobs Title: technology lead Company: Tiger Analytics
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