Datamart Developer - Data Warehousing & ETL

Position : Data Mart Developer. Exp : 5 years. Location : Jaipur. Data Warehousing Concepts : - Data Warehousing Principles : Strong understanding of data warehousing concepts such as star schema, snowflake schema, fact tables, and dimension tables. - ETL Processes : Proficiency in Extract, Transform, Load (ETL) processes to extract data from multiple sources, transform it for analysis, and load it into data marts. - OLAP vs OLTP : Knowledge of Online Analytical Processing (OLAP) and Online Transactional Processing (OLTP) systems and their roles in data marts and data warehouses. Data Modeling & Architecture : - Dimensional Modeling : Experience in dimensional modeling to design fact and dimension tables, ensuring optimal query performance for business intelligence reports. - Logical & Physical Data Models: Ability to design both logical and physical data models, ensuring they meet the business requirements while being optimized for performance. - Metadata Management : Expertise in managing metadata to define data structures and relationships for better accessibility and usability within data marts. Database & SQL Expertise : - Database Technologies : Strong experience with databases like SQL Server, Oracle, MySQL, PostgreSQL, or cloud-based databases like Amazon Redshift or Azure SQL Database. - SQL Mastery : Advanced skills in SQL for querying, data manipulation, indexing, joins, stored procedures, and performance tuning. - Data Partitioning : Knowledge of partitioning data for performance optimization, especially in handling large datasets. ETL Development : - ETL Tools : Proficiency with ETL tools like SSIS (SQL Server Integration Services), Informatica, Talend, Pentaho, or Azure Data Factory for designing, developing, and automating data pipelines. - Data Transformation : Strong capability in transforming raw data into the desired format, including data cleaning, normalization, and aggregation. - Error Handling & Monitoring : Experience in building robust ETL processes with error handling, logging, and monitoring features to ensure data integrity and reliability. Data Integration & Source Systems : - Data Integration : Ability to integrate data from multiple, heterogeneous sources such as databases, flat files, APIs, and cloud services. - Real-Time Data Integration : Experience with real-time or near-real-time data integration using tools like Kafka, AWS Kinesis, or Azure Stream Analytics. - APIs & Web Services : Knowledge of REST APIs and SOAP web services for integrating external data sources into data marts. Performance Tuning & Optimization : - Query Optimization : Expertise in optimizing SQL queries and ETL jobs to improve performance, reduce query execution time, and manage large datasets effectively. - Data Mart Optimization : Skills in tuning data mart structures (fact tables, indexes, partitions) to ensure high performance for reporting and analytics purposes. - Indexing & Partitioning: Knowledge of indexing strategies and partitioning techniques to improve query performance, especially for large datasets. Data Governance & Security : - Data Governance : Understanding of data governance practices, including data quality, data stewardship, and data lineage. - Data Security & Compliance : Proficiency in implementing data security measures such as access controls, data masking, and encryption to protect sensitive information. - Role-Based Access : Implementing role-based access control (RBAC) and user permissions for secure access to data marts. Business Intelligence Tools : - BI Tools Integration : Experience in integrating data marts with BI tools like Power BI, Tableau, or Looker to enable reporting and dashboard creation. - Support for Analytics : Collaborating with BI and analytics teams to ensure data marts are structured in a way that supports efficient reporting and analytics queries. Cloud Technologies & Big Data : - Cloud Platforms : Experience with cloud-based data warehousing solutions like Amazon Redshift, Google BigQuery, Snowflake, or Azure Synapse Analytics. - Big Data Tools : Familiarity with big data technologies like Hadoop, Hive, or Spark for handling large-scale data and integrating them into data marts. - Data Lakes : Knowledge of integrating data lakes with data marts for centralized data storage and analysis, ensuring consistency between unstructured and structured data. Scripting & Automation : - Scripting Languages : Proficiency in scripting languages like Python, Shell scripting, or PowerShell for automating ETL processes and data mart operations. - Automation of Data Pipelines : Experience in automating data pipelines using tools like Apache Airflow, scheduling ETL jobs, and monitoring data flows. Version Control & CI/CD : - Version Control : Familiarity with version control systems like Git for managing ETL scripts, database schema changes, and collaborative development. - CI/CD for Data Pipelines : Experience in implementing Continuous Integration/Continuous Delivery (CI/CD) pipelines for deploying changes to ETL processes and data marts. Soft Skills : - Collaboration : Ability to work closely with data analysts, data scientists, business analysts, and other stakeholders to ensure that the data mart meets their needs. - Communication Skills : Strong written and verbal communication skills to translate business requirements into technical specifications for data marts. - Problem Solving : Proficient in diagnosing and resolving data issues, such as data discrepancies, inconsistencies, or performance bottlenecks. Certifications & Continuous Learning : - Relevant Certifications : Certifications in data warehousing, ETL tools, or cloud platforms (e.g, AWS Certified Solutions Architect, Microsoft Azure Data Engineer, etc. - Continuous Learning : Staying updated with the latest developments in data warehousing, ETL technologies, and cloud data solutions. (ref:hirist.tech)

Location: rajasthan, IN

Posted Date: 2/14/2025