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For Data analysts , major challenges are to build jugaar or hacks to solve the business problems 1) Sql query writing is easy but logic building is really tough 2) we builds hacks/ jugaar 😅 to cover the gap between data engineering and ground teams 3) Creating multiple temp tables to exclude holidays, areas and doing a lot of mapping to make the data accurate and logically sound 4) Building different mappings using either temp table or case when function according to the business requirements. Due to this , new roles are coming into trend like analytics engineer, busineer engineer which directly connect with the onground teams while building data pipelines

For a backend engineer, you may need to know most of it. As a data analyst, you may need to have a good understanding of DQL. Select the topics that are most relevant to you. There are 5 components of the SQL language: -> DDL: data definition language, such as CREATE, ALTER, DROP -> DQL: data query language, such as SELECT -> DML: data manipulation language, such as INSERT, UPDATE, DELETE -> DCL: data control language, such as GRANT, REVOKE -> TCL: transaction control language, such as COMMIT, ROLLBACK Source of information + cute gif: https://lnkd.in/eiQXNa7z #backend #sqldeveloper #backenddevelopment #databasemanagement #sqlprogramming

Hi There, Greetings from iTek People Inc! Role: Data Scientist Location: Remote Experience: Strictly 10+ Years (No GC under 15 years) Job Description: Task #1: Data Mining using state-of-the-art methods Deliverables: Useable and understandable product results will be derived from data mining. Acceptance Criteria: The deliverable must meet the following acceptance criteria in addition to compliance with the technical approach outlined below: Must be formatted in a way that is acceptable to Inspectors for use in the field with data that is helpful and drives intel-based targeting and investigations.   Task #2: Provide third party data, where applicable, to enhance product results Deliverables: Complete and comprehensive product results utilizing internal data and external data where authorized and acceptable. Acceptance Criteria: The deliverable must meet the following acceptance criteria in addition to compliance with the technical approach outlined below.   Task #3: Enhance data collection procedures Deliverables: Provide a technologically advanced data collection system. Acceptance Criteria: The deliverable must meet the following acceptance criteria in addition to compliance with the technical approach outlined below: Criteria would include the automation process for collecting data for building analytical systems. The data must be processed, cleansed and the integrity of the data must be verified.   Task #4: Conduct ad-hoc analysis and present results Deliverables: Product outputs from ad-hoc analysis requests from CI2 Acceptance Criteria: The deliverable must meet the following acceptance criteria in addition to compliance with the technical approach outlined below: The product must contain an output presented in a variety of formats for clear understanding by intended audiences.   Task #5: Create automated anomaly detection systems Deliverables: Automated anomaly detection system. Acceptance Criteria: The deliverable must meet the following acceptance criteria in addition to compliance with the technical approach outlined below: Anomaly detection system with a built-in process to track performance of the system.   Task #6: Assist in general IT support as needed Deliverables: Successful assistance in general IT support within the CI2 and CI2 NTC environment Acceptance Criteria: The deliverable must meet the following acceptance criteria in addition to compliance with the technical approach outlined below:   Task #7: Continue to enhance a Cloud resource hosting solution Deliverables: Fully Cloud resource-hosted and accessible tools including APOLLO, HYDRA and ORION. Acceptance Criteria: The deliverable must meet the following acceptance criteria in addition to compliance with the technical approach outlined below: Tools must be hosted and made accessible to Inspectors in the field. Thanks, Harsha harsha@itekpeople.com 512 999 7192.

Analytics engineers are and will be very much needed in data teams. Analytics engineers are the mix of data engineers and data analysts. They can communicate with the business and translate business requirements into data models that empower downstream data needs. They're familiar with software engineering practices such as source control, testing, and CI/CD. They're technical enough to implement end-to-end data pipelines if they need to. What do you think a data team will look like in the future? #analyticsengineering #dataanalytics

What is a Big Data Engineer? The demand for skilled Big Data engineers is skyrocketing. This role requires a unique blend of technical expertise and soft skills. Here's what you need to know about becoming a Big Data Engineer: 1. Programming languages: Python, Java, Scala, and R 2. Databases: Relational, NoSQL, and cloud-based 3. Data engineering tools: ETL, data integration, data quality, and data governance 4. Problem-solving: Creativity, logic, and critical thinking 5. Communication and teamwork: Collaboration with data analysts, scientists, and business stakeholders Want to learn more about this exciting career? Click here for a comprehensive guide on becoming a Big Data Engineer. https://lnkd.in/dJRbyY9A

A comprehensive summary of SQL commands and functions with examples, SQL is widely used in different roles daily job, which includes  Backend Developer, QA Engineer, Database Admin, Data Architect, ETL Developer, System Engineer, Data Analyst, Data Scientist, System Admin… The responsibilities can be extended by work demands, but generally, Data Analysts and Scientists are the end users of a database. They use SQL to retrieve data (main use), combine multiple sources, create their own table or test environment, and sometimes write more complex queries for analysis. #dataanalysis #datascience #sql #bigdata #programming

Hi There, Greetings from iTek People Inc! Role: Data Scientist Location: Remote Experience: Strictly 10+ Years (No GC under 15 years) Job Description: Task #1: Data Mining using state-of-the-art methods Deliverables: Useable and understandable product results will be derived from data mining. Acceptance Criteria: The deliverable must meet the following acceptance criteria in addition to compliance with the technical approach outlined below: Must be formatted in a way that is acceptable to Inspectors for use in the field with data that is helpful and drives intel-based targeting and investigations. Task #2: Provide third party data, where applicable, to enhance product results Deliverables: Complete and comprehensive product results utilizing internal data and external data where authorized and acceptable. Acceptance Criteria: The deliverable must meet the following acceptance criteria in addition to compliance with the technical approach outlined below. Task #3: Enhance data collection procedures Deliverables: Provide a technologically advanced data collection system. Acceptance Criteria: The deliverable must meet the following acceptance criteria in addition to compliance with the technical approach outlined below: Criteria would include the automation process for collecting data for building analytical systems. The data must be processed, cleansed and the integrity of the data must be verified. Task #4: Conduct ad-hoc analysis and present results Deliverables: Product outputs from ad-hoc analysis requests from CI2 Acceptance Criteria: The deliverable must meet the following acceptance criteria in addition to compliance with the technical approach outlined below: The product must contain an output presented in a variety of formats for clear understanding by intended audiences. Task #5: Create automated anomaly detection systems Deliverables: Automated anomaly detection system. Acceptance Criteria: The deliverable must meet the following acceptance criteria in addition to compliance with the technical approach outlined below: Anomaly detection system with a built-in process to track performance of the system. Task #6: Assist in general IT support as needed Deliverables: Successful assistance in general IT support within the CI2 and CI2 NTC environment Acceptance Criteria: The deliverable must meet the following acceptance criteria in addition to compliance with the technical approach outlined below: Task #7: Continue to enhance a Cloud resource hosting solution Deliverables: Fully Cloud resource-hosted and accessible tools including APOLLO, HYDRA and ORION. Acceptance Criteria: The deliverable must meet the following acceptance criteria in addition to compliance with the technical approach outlined below: Tools must be hosted and made accessible to Inspectors in the field. Thanks, Harsha harsha@itekpeople.com 512 999 7192.

To be successful in their role, Data Engineers need a mix of technical, functional, and soft skills. * Technical Skills: Include working with different operating systems and infrastructure components such as virtual machines, networks, and application services. It also includes working with databases and data warehouses, data pipelines, #ETL tools, big data processing tools, and languages for querying, manipulating, and processing data. * An understanding of the potential application of data in business is an important skill for a data engineer. Other functional skills include the ability to convert business requirements into technical specifications, an understanding of the software development lifecycle, and the areas of data quality, privacy, security, and governance. * Soft Skills: include interpersonal skills, the ability to work collaboratively, teamwork, and effective communication.  #python #dataengineer #edx #webscraping #dataautomation #dataengineering

Some of the frequent mistakes that can hinder a candidate's chances of securing a data engineering role: Inadequate Preparation for Technical Questions: Candidates sometimes focus too much on one aspect (like SQL) and neglect others such as data modelling, ETL processes, or performance optimization. Data engineering encompasses a broad set of skills, and demonstrating competence across this spectrum is crucial. Lack of Understanding of Data Infrastructure: A surprising number of candidates lack a deep understanding of the data infrastructure they’ve worked on. It’s important to not just know how to use the tools but also how these tools fit into the larger picture of data flow and operations. Poor Problem-Solving Approach: When presented with a problem scenario, some candidates jump to solutions without first methodically analyzing the issue or asking clarifying questions. A structured approach to problem-solving is highly valued in these roles. Weakness in Programming Skills: Data engineering requires strong coding skills, typically in Python or Java. Some candidates, especially those transitioning from data analysis or other fields, might under-prepare this crucial skill. Failure to Demonstrate Actual Experience: Practical, hands-on experience with real systems is often underestimated. Candidates sometimes fail to convincingly discuss their experience in dealing with real-world data engineering challenges. #data #engineering #guidance #spark #dataengineering #pyspark #aws