4 Star Schemas Jobs

The Data Analyst II plays a critical role in engaging with stakeholders and technical team members to execute requirement gathering, documentation of data flows, mapping, extraction, transformation, visualizations, and analytical data analysis. You will work closely with cross-functional teams, including IT and business stakeholders to ensure seamless and efficient data flow, report generation, visualizations, and data analysis. Collaboration with various departments to ensure data accuracy, integrity, and compliance with established data standards is essential. This role reports to the BEST Data Services Senior Manager in the Business Enterprise Systems Technology department. A successful Data Analyst must take a hands-on approach, ensuring the highest quality solutions are provided to business stakeholders, with accurate development, documentation, and adherence to deadlines. This role will also work with key stakeholders across the organization to drive enhancements to a successful implementation and ensure all reporting and analytics meet requirements and are deployed and implemented properly. Responsibilities include engaging with multiple teams to understand reporting and analytical requirements, collaborating with business stakeholders to understand their requirements and translate them into reporting specifications, retrieving and manipulating data from various sources using SQL and ETL tools, cleansing, transforming, and enriching data for accuracy and consistency, developing and maintaining data models to support reporting needs, creating standardized reports and dashboards using tools like Power BI and Tableau, designing and building data visualizations, conducting ad-hoc analysis, ensuring data security and compliance, aligning with business objectives, working on data migration, communicating project status, maintaining detailed documentation, providing post-migration support, and collaborating with technical teams for solutions. Required Knowledge/Skills/Abilities: - Minimum of 1 year of hands-on Data Analyst experience - Proficiency in SQL for data extraction, manipulation, and analysis - Minimum of 1 year of experience with data visualization tools like Power BI - Minimum of 2 years" experience with Python libraries for data visualization and analysis - Strong understanding of data structures, databases, and data models - Excellent communication skills - Proficiency in designing and implementing process workflows and data diagrams - Proven Agile development experience - Excellent problem-solving skills and innovative thinking - Exceptional communication, analytical, and management skills - Ability to present technical concepts to both business executives and technical teams - Able to manage daily stand-ups, escalations, issues, and risks - Self-directed, adaptable, empathetic, flexible, and forward-thinking - Strong organizational, interpersonal, and relationship-building skills - Passionate about technology, digital transformation, and business process reengineering,

As a Data Modeler specializing in Hybrid Data Environments, you will play a crucial role in designing, developing, and optimizing data models that facilitate enterprise-level analytics, insights generation, and operational reporting. You will collaborate with business analysts and stakeholders to comprehend business processes and translate them into effective data modeling solutions. Your expertise in traditional data stores such as SQL Server and Oracle DB, along with proficiency in Azure/Databricks cloud environments, will be essential in migrating and optimizing existing data models. Your responsibilities will include designing logical and physical data models that capture the granularity of data required for analytical and reporting purposes. You will establish data modeling standards and best practices to maintain data architecture integrity and collaborate with data engineers and BI developers to ensure data models align with analytical and operational reporting needs. Conducting data profiling and analysis to understand data sources, relationships, and quality will inform your data modeling process. Your qualifications should include a Bachelor's degree in Computer Science, Information Systems, Data Science, or a related field, along with a minimum of 5 years of experience in data modeling. Proficiency in SQL, familiarity with data modeling tools, and understanding of Azure cloud services, Databricks, and big data technologies are essential. Your ability to translate complex business requirements into effective data models, strong analytical skills, attention to detail, and excellent communication and collaboration abilities will be crucial in this role. In summary, as a Data Modeler for Hybrid Data Environments, you will drive the development and maintenance of data models that support analytical and reporting functions, contribute to the establishment of data governance policies and procedures, and continuously refine data models to meet evolving business needs and leverage new data modeling techniques and cloud capabilities.,

You should have an in-depth understanding of data management, including permissions, recovery, security, and monitoring. You must also possess strong experience in implementing data analysis techniques such as exploratory data profiling. Additionally, you should have a solid grasp of design patterns and hands-on experience in developing data pipelines for batch processing. Your role will require you to design and develop ETL processes that populate star schemas using various source data for data warehouse implementations supporting a product on both cloud and on-premise environments. You should be able to actively participate in the requirements gathering process and design business process dimensional models. Collaboration with data providers to address data gaps and make adjustments to source-system data structures for seamless analysis and integration with other company data will be a key responsibility. A basic understanding of scripting languages like Python is necessary for this role. Moreover, you should be skilled in both proactive and reactive performance tuning at the instance-level, database-level, and query-level to optimize data processing efficiency.,

You are an experienced and results-driven ETL & DWH Engineer/Data Analyst with over 8 years of expertise in data integration, warehousing, and analytics. Your role involves having deep technical knowledge in ETL tools, strong data modeling skills, and the capability to lead intricate data engineering projects from inception to implementation. Your key skills include: - Utilizing ETL tools such as SSIS, Informatica, DataStage, or Talend for more than 4 years. - Proficiency in relational databases like SQL Server and MySQL. - Comprehensive understanding of Data Mart/EDW methodologies. - Designing star schemas, snowflake schemas, fact and dimension tables. - Experience with Snowflake or BigQuery. - Familiarity with reporting and analytics tools like Tableau and Power BI. - Proficient in scripting and programming using Python. - Knowledge of cloud platforms like AWS or Azure. - Leading recruitment, estimation, and project execution. - Exposure to Sales and Marketing data domains. - Working with cross-functional and geographically distributed teams. - Translating complex data issues into actionable insights. - Strong communication and client management abilities. - Initiative-driven with a collaborative approach and problem-solving mindset. Your roles & responsibilities will include: - Creating high-level and low-level design documents for middleware and ETL architecture. - Designing and reviewing data integration components while ensuring compliance with standards and best practices. - Ensuring delivery quality and timeliness for one or more complex projects. - Providing functional and non-functional assessments for global data implementations. - Offering technical guidance and support to junior team members for problem-solving. - Leading QA processes for deliverables and validating progress against project timelines. - Managing issue escalation, status tracking, and continuous improvement initiatives. - Supporting planning, estimation, and resourcing for data engineering efforts.,