Data Engineer Jobs
A Data Engineer is highly skilled in analytics, data management, and developing data architecture, making them the perfect addition to any organization in need of real-time insights. Data Engineers create the pipelines and data architecture necessary for Business Intelligence teams to access archives of data from which to analyze trends, providing them visibility into the current state of their business. In short, Data Engineers make it possible for companies to make informed decisions based on data quickly and accurately.
Here's some projects that our expert Data Engineers made real:
- Developed ETL pipelines from sources such as APIs, web services and databases, ensuring efficient data extraction while converting source data into desired formats.
- Designed custom databases and data models e.g. NoSQL and Big Data technologies such as Hadoop and Hive to store large datasets.
- Optimized data analysis processes using Python libraries such as pandas, numpy and scikit-learn to generate pattern recognition algorithms.
- Implemented advanced analytics techniques such as clustering analysis and forecasting models at scale.
- Automated data pipeline processes using source control platforms such as GIT, allowing teams to access and modify pipelines without breaking production code.
Data Engineering is an essential practice for any organization looking to analyze their historical business performance and make informed decisions on real-time data. The projects here are a testament to the power of Data Engineering; our experts have proved that with the right skillset businesses can cut through their complex datasets with ease – letting them focus on how best to use their crisp new insights. If you’re looking for an experienced and reliable comparison of your data then we invite you post your project now and hire a Data Engineer on Freelancer.com today!
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1 Problem Statement and Datasets In this assignment, you will implement Vector Based Models to rank academic research papers. Refer to the code and dataset that can be downloaded from the zip file here. The code is written and tested in Python 3.10. The dataset consists of 8K research papers (or documents) and 100 queries downloaded from Semantic Scholar 1 . The zip file consists of: 1. file 2. file 3. 4. s2 folder with: (a) s2/s2 : All (8K) research papers in the corpus. Each research paper has 3 fields. i. docno: A unique document id for each research paper ii. title: Title of the research paper iii. paperAbstract: Abstract of the research paper A single paper entry looks like: { "docno": "6e4eddf4d6671c37537bb5d1c9623353b62e8531", "title": [ "Dualit...
The data pipeline type we want is Appsflyer (app) / GA4 (web) data -> GCP -> Big Query. I will briefly share the necessary information at this time, so please check it. 1. Copying (linking) AppsFlyer and GA4 data to GCP a. GCP is in a contracted state, and environment settings are required (security settings, etc. required) 2. AppsFlyer a. All data supported by AppsFlyer & Data Locker must be linked b. Both raw data and aggregate data must be linked (all data provided by AppsFlyer, including SKAN) 3. GA 4 a. GA 4 is contracted and also includes GA 360 integration b. Both raw data and aggregate data must be linked (including SKAN) 4. Common to AppsFlyer & GA 4 a. Must be linked to a table to be searchable in Big Query b. All periods of saved data must be...
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