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A Comprehensive Look at Business Intelligence Careers Discover how each role uniquely contributes to harnessing the power of information. From Business Intelligence Analysts crafting actionable insights to Reporting Analysts driving strategic decisions, each role is a key player in the data landscape. SQL Developers master data organization, ETL Developers ensure seamless integration, and Business Intelligence Developers bring it all together. Eager to start a career in Business Intelligence? Talk to an advisor to learn how your skills can be accelerated in this career: https://lnkd.in/e7gZGbv #DataCareers #BusinessIntelligence #DataAnalytics #SQLDeveloper #ETLDeveloper #CareerGuide

Opportunity

#Hiring SAS Fraud Management Developer #sas #fraudmanagement #sasdeveloper #sasprogrammer share resume at rsingh@reqroute.com ONSITE (Riverside, Illinois) If candidate is stellar, may be able to negotiate with client on remote. Top 3 Must Have's: 1. SAS Fraud management – more back end developer 2. JAVA 3. Unix Shell Scripting JD from HM: SAS Fraud Management Programming and Development: Develop, optimize, and maintain SAS programs and scripts for data extraction, transformation, and analysis. SAS Backend developer who can able to implement the software installations, understand different component of SAS FM, and how they work with each other. Java experience Unix shell scripting experience. Convert existing SAS FM code into SAS Visual Analytics reports and dashboards. Data Manipulation and Transformation: Perform data cleansing, transformation, and integration to prepare data for analysis. Connect to new data sources and maintain data integrity. Analytics and Insights: Implement statistical and predictive models using SAS to derive insights and solve complex business problems. Create informative and interactive data visualizations using SAS or similar tools. Collaboration and Documentation: Work closely with cross-functional teams, including data scientists, analysts, and business stakeholders, to understand their requirements and provide analytical solutions. Document SAS code, processes, and methodologies to ensure reproducibility and knowledge sharing within the team. Performance Optimization and Quality Assurance: Identify opportunities to optimize code and processes for efficiency and scalability. Conduct quality checks and testing to ensure data accuracy and model reliability. Compliance and Security: Adhere to data privacy and security guidelines while handling sensitive information.

𝐃𝐚𝐭𝐚 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 𝐚𝐧𝐝 𝐃𝐚𝐭𝐚 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 - how do we approach this? The Data Quality and Governance market has seen an exponential increase in hiring demand, and there are 2 main root causes. 👇 𝐁𝐨𝐫𝐝𝐞𝐫𝐞𝐚𝐮𝐱 - the BDX source data is inaccurate, incomplete, or simply not lining up. This data comes from multiple different market sources, so managing these pipelines is crucial to keep reporting on point. 𝐃𝐚𝐭𝐚 𝐚𝐧𝐝 𝐓𝐞𝐜𝐡 𝐭𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 - upscaling your data and technology capabilities will inevitably uncover underlying process and data management issues, so new initiatives will be needed to resolve and prevent them. So, I'm looking to speak to data professionals with great experience in: 🔵 Bordereaux Data 🟢 Data Quality tools such as Informatica or DQPro 🔵 Analytics - used to looking into the granular detail of data 🟢 Coding - a background in utilising SQL coding and confident in reviewing code 🔵 Data Management - you know what good looks like! 🟢 Insurance - a great understanding of the Lloyd's & Company markets If you're comfortable with any of the above, I want to speak with you! How do you approach Data Quality & Governance? #DataQuality #DataGovernance #Hiring #DataManagement

Jobs in SQL involve tasks like querying databases, creating and managing database structures, ensuring data integrity, and optimizing queries for performance. Common roles include SQL Developer, Database Administrator (DBA), Data Analyst, and Business Intelligence (BI) Developer. These roles require proficiency in SQL to retrieve and manipulate data, design and implement database solutions, and support data-driven decision-making processes within organizations. #SQL #Database #DataScience #DataAnalytics #SQLDeveloper #DataEngineer #BigData #DatabaseManagement #SQLServer #DataAnalysis #Tech #Programming #Coding #DataVisualization #BusinessIntelligence

"Unlock the power of data analysis with IT Wave Solutions! 📊 Dive into real-world training led by seasoned professionals and embark on your journey to becoming a proficient data analyst. 🚀 #DataAnalysis #ITWaveSolutions #CareerDevelopment" #dataanalytics #datadriven #onlinecourse #training #opportunity #jobs #dataanalyst #hiring #traininginnoida #noida #itcompany #itconsulting #data

This is what happens when you have just one analyst handling all data-related tasks in a business: They stop being an analyst. They spend a lot of time: - answering user queries - performing manual data saves - exploring connections to databases - defining the right metrics/KPIs - creating and updating reports - training report users - building logic in data pipelines - addressing data quality issues - resolving data refresh errors They end up doing everything except analysing data and driving commercial decisions. Multitasking and constant busyness stop creative thinking. That was my firsthand experience in my first-ever data role. It was incredibly rewarding to start with as you get to do everything, but as the scope and complexity increased over time, scaling became very challenging. This may be manageable for small businesses, but medium to large businesses should consider investing early in dedicated role(s): - Business Analysts for requirements gathering - Data Engineers for data warehousing and ETL processes - Data Modelers or BI Report Specialists for creating enterprise data models and tool-specific report building So data analysts can be analysts. Otherwise, they become report-churning zombies, spending most of their time behind screens rather than engaging with colleagues on real business problems and insights. This cycle only worsens over time if early investments in specific data roles are not made. #dataanalyst #datacareer #dataanalystjobs #datateams

Being a data analyst, I love SQL. It is such a satisfying experience for me when I work with SQL. I love cleaning and pre-processing the data, transforming it, performing data validation, performing EDA on data, and diving deep into it. 𝗜 𝗿𝗲𝗮𝗹𝗹𝘆 𝗲𝗻𝗷𝗼𝘆 𝘁𝗮𝘀𝗸𝘀 𝘀𝘂𝗰𝗵 𝗮𝘀: • Running SQL queries to get the result. • Creating solutions and actionable insights for business problems. • Optimizing the queries for better performance. • Creating Views and performing detailed operations on it and much more. • Using CTEs to ensure improved query execution. • Using window functions to perform various operations on data according to need. • Ensuring that the subqueries are not degrading the performance and if they are, replacing it with CTEs etc. • Performing effective Joins between tables. • I love each aspect of SQL. Also, tell me what you love about SQL in the comments! #SQLLove #DataWithSQL #SQLForBusiness #SQLAnalytics #DataTransformation #SQLQueries #CTEinSQL #WindowFunctionsSQL #SQLPerformanceTuning #DataCleaningSQL #SQLJoins #SQLViews #USAnalytics #UKDataExperts #EuropeDataSkills #SQLCommunity #SQLInAction #BusinessInsightsSQL #USDataAnalytics #UKDataProfessionals #EuropeDataExperts #USRecruitment #UKTechJobs #EuropeAnalyticsJobs #USEmployers #UKDataCareers #EuropeDataOpportunities #USDataTalent #UKBusinessAnalytics #EuropeTechTalent #UKSQLJobs #USDataJobs #EuropeSQLProfessionals #UKHiring #USAnalyticsJobs #EuropeTechJobs

Continuing my series of Understanding the market as a Business Analyst/Data Analyst: Along with database understanding it is vital to understand data discrepancies and data redundancies performing data gap analysis of the statistical data. As a Data Analyst in order to understand data it is important to know how to design data models, performing data entry into the models performing, scheduling data backups using T-SQL or MySQL commands like Data Definition Language (DDL). Models could also include columns that need combining or splitting up information for which utilization of Data Manipulation Language (DML) is important. As a Data Analyst it is also important to consider factors related to data security and data integrity and in order to understand this irrespective of being a Data Analyst or a Business Analyst it is vital to differentiate between functional and non-functional requirements of the system, data or any project. Functional requirements will talk about what the system or data does while non-functional requirements focus on how the system works. Examples of functional requirements would be data flowing between units, controls in the system while non-functional requirements would be scalability, reliability and robustness.   #businessanalyst #markettrends #dataanalyst #datapreparation #datavisualization #datavisualizationtools #datamodeling #dataanalystjobs #businessanalystjobs

Any one interested to join Interview preparation and Mock interviews program #DM me. Real time and Scenario Based ETL/DWH Testing Interview Questions 1. How do you handle testing of data loads during peak hours in a data warehouse environment?   2. Have you worked on testing data migration from one data warehouse to another? If so, what challenges did you face?   3. Can you provide an example of a complex data validation scenario you have encountered in data warehouse testing and how you resolved it?   4. Have you worked on testing data transformations and validations in a data warehouse? If so, can you give an example?   5. Can you write a SQL query to retrieve data from multiple tables in a data warehouse for reporting purposes?   6. Write a SQL query to identify customers who have not made any purchases in the last six months in a data warehouse.   7. How would you join multiple tables in a data warehouse to retrieve customer information along with their order details?   8. Write a SQL query to calculate the total sales amount for each product category in a data warehouse.   9. Write a SQL query to find the top 5 best-selling products in a data warehouse based on the total sales amount. #ETL #TESTING #Datawarehouse #testing #data #SQL #transformations #usa #UK #Canada #hiring #jobs #ITJOBS