Datamart Developer - Data Warehousing & ETL (5-8 yrs)

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.