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Good knowledge and Proven practical experience in migrating relational data base from on-prem to cloud OR to build cloud-based data lake using either Big Data / Azure Synapse / Databricks / Snowflake.
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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.
$75,600 - $172,000 a yearExpandApply NowActive JobUpdated 10 days ago - UpvoteDownvoteShare Job
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4+ years of professional experience as a data scientist in the SaaS industry. Experience with dbt or similar analytics workflow tools a big plus. We are a top-notch data organization with a great culture and have the same high standards with our code, systems, practices, and people.
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The big data architect will support the development of autonomous driving software solutions by playing the role of subject matter expert for the big data systems, underlying data and data products.
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As a data engineer, you know that organizing big data can yield pivotal insights when it's gathered from disparate sources. Work with us to use big data for good. Experience with distributed data and computing tools, including Spark, Databricks, Hadoop, Hive, AWS EMR, or Kafka.
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3 years big data experience Expertise in Java, Python, R, and proficiency in big data technologies such as Spark, Databricks, etc. Implement and optimize data processing workflows using Spark, Databricks, and other big data frameworks.
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Comfortability with data warehouses and big data technologies such as Redshift, Snowflake, Big Query, Presto/Trino, Athena, Spark, DBT. We’re looking for an experienced Data Scientist to support our Go To Market teams as they expand across product categories and geographic markets.
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Experience with data-intensive applications, big data pipelines and analytics, having used tools such as Snowflake, DataBricks, Amplitude and Looker. Develop and support cloud-based big data pipelinesUse analytics to understand product behavior and motivate data-driven decisions.
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Big Data Technologies : Experience with big data technologies and tools such as Apache Spark, Hadoop, Apache Cassandra, and distributed computing frameworks for handling large-scale datasets.
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Experience with data warehousing using AWS Redshift, MySQL, or Snowflake. Ever-expanding technology like IoT, machine learning, and artificial intelligence means that there s more structured and unstructured data available today than ever before.
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Experience with NoSQL databases such as mongoDB, Cassandra or similar is a plusMinimum of 4 years of experience in developing data ingestion, data processing and analytical pipelines for big data, relational databases, NoSQL, data lake and data warehouse solutionsMinimum of 4 years of hands-on experience in Big Data technologies such as, PySpark, Spark/SparkSQL, Dask or similar distributed compute frameworks.
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Data science techniques such as predictive analytics, machine learning, natural language processing and business intelligence, including use of big and non-tabular data. Advanced skills in statistical analysis using Stata, SPSS, R, SAS, or SQL for the purpose of Advanced skills in using data visualization platforms such as advanced Excel visualizations, Tableau, PowerBI, Metabase, Google Data Studio, etc.
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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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Exposure to big data technologies such as Spark, AI, Data Science, Data Engineering, Hadoop, Cassandra, etc. Minimum 10 years in a technical role delivering projects in Data Analytics, Machine Learning, Cloud Computing Platforms, and Service-oriented architecture, with a preference for Large Language Model (LLM) technology.
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Data Engineering : Knowledge of data engineering principles and best practices for data acquisition, transformation, storage, and retrieval in AI applications. Cloud Computing Platforms : Familiarity with cloud platforms such as Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP) for AI development, deployment, and scaling.
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big data jobs Title: scientist Company: Brainstation
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Virtual Reality Job Interviews
With the advent of desktop computers, the arduous task of scouring through weekly job classifieds became a thing of the past. The mid-1990s brought about a new era where job seekers could easily search and apply for jobs online. The introduction of AOL's Instant Messaging feature provided an even faster means for employers and candidates to communicate and schedule interviews. As smartphones became more pervasive in the early 2000s, hiring managers increasingly used phone calls for screening and interviewing candidates. Despite this trend, over 80% of interviews still took place in person.
A Potential TikTok Ban?!
As you may already know, there has been a lot of talk lately about the possibility of a TikTok ban. While this has not yet come to fruition, it's important to consider the implications this could have for businesses and recruiters who rely on TikTok as a platform to market their brand, recruit new talent, and connect with their audience.
The Effects of Workplace Racism and Sexism
One day it's a covert statement to a mother returning to work after maternity leave. Another day it's a lingering gaze at an employee enjoying a culturally rich meal. These microaggressions (or sometimes macroaggressions) can take an employee from a confident, high-performer to one that feels insecure being themselves at work. Your employees engage with people with different ideas and feel most comfortable and valued when they can work without losing their cultural, racial, and gender identity. While most employers know this, why have workplace racism and sexism often been neglected?
When Rage Applying Strikes: How to Identify Unserious Candidates
As the job market remains highly competitive, we have seen a surge in "rage applying." This is when candidates apply to multiple jobs, often without considering whether they are truly interested in the role. Rage applying goes hand-in-hand with quiet quitting. Often, employees want to entertain the thoughts and feelings of leaving their job, but they aren't necessarily serious about leaving yet. Meanwhile, other employees engaging in this trend are actually trying to find a better role. As a recruiter, it can be hard to identify who are the real applicants in a sea full of quiet quitters, but understanding rage applying and identifying red flags will certainly help.
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In today's competitive job market, writing quality job ads is critical for attracting top talent to your organization. While networking and candidate referrals are prime real estate for finding qualified candidates, nothing beats the tried-and-true method of writing an extraordinary job ad. But while writing a great job ad is the first step, what's more important is increasing visibility. You could have the most detailed, well-written ad on the internet, but if no one sees it, then you are wasting time (and potentially money!). Employers often believe that job boards are the root of the problem, but you can learn how to increase job ad exposure by tweaking a few steps of your recruitment process.
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The job market has shifted significantly in recent years. The accelerated adoption of technology has not only pushed many companies into remote working arrangements but also increased the availability of supporting tools and technologies (i.e., video conferencing and collaboration software).