Job Summary:
We are seeking an Expert Data Warehouse Analyst to architect, optimize, and manage large-scale enterprise data warehouse (EDW) solutions. The ideal candidate will have extensive expertise in SQL, ETL processes, cloud-based data warehousing, data governance, and advanced analytics. This role requires leading high-impact data warehouse projects, optimizing data performance, implementing security measures, and driving business intelligence (BI) strategies for data-driven decision-making.
Key Responsibilities:
- Architect, design, and optimize large-scale data warehouse solutions for enterprise applications
- Develop, implement, and manage cloud-based data warehouses (AWS Redshift, Google BigQuery, Snowflake, Azure Synapse)
- Optimize complex SQL queries, indexing strategies, partitioning, and materialized views for high-speed performance
- Design and maintain ELT/ETL pipelines, managing batch processing, real-time streaming, and workflow orchestration
- Ensure data governance, security, and compliance with regulations such as GDPR, HIPAA, CCPA, and PCI-DSS
- Implement scalable data integration solutions, connecting structured and unstructured data from multiple sources
- Develop OLAP cubes, data marts, and dimensional models to support business intelligence and reporting
- Lead high-performance teams, mentor junior data professionals, and enforce data management best practices
- Collaborate with data engineers, analysts, DevOps, and business stakeholders to define data strategies
- Manage automation for data ingestion, transformation, and quality control using AI/ML-driven techniques
- Implement monitoring and alerting systems to detect and resolve data quality and performance issues
- Stay ahead of emerging trends in big data, data lakehouses, AI/ML, and predictive analytics
Skills and Knowledge Required:
- Expert-level SQL proficiency (SQL, PL/SQL, T-SQL) and query optimization techniques
- Extensive experience in cloud-based data warehouse platforms (AWS Redshift, Google BigQuery, Snowflake, Azure Synapse)
- Mastery of ETL/ELT processes, data ingestion frameworks, and real-time streaming technologies (Kafka, Spark, Flink)
- Advanced knowledge of data modeling techniques (dimensional modeling, data vault, fact & dimension tables)
- Deep understanding of data security, encryption, role-based access control (RBAC), and audit logging
- Experience with AI-driven analytics, predictive modeling, and ML integrations in data warehousing
- Strong understanding of data pipeline orchestration tools (Apache Airflow, AWS Glue, dbt, Prefect)
- Proficiency in Python, Shell Scripting, or PowerShell for automation and data manipulation
- Experience in DevOps for data warehouse automation, including CI/CD, Terraform, and Kubernetes
- Hands-on experience with NoSQL and hybrid data architectures (MongoDB, Cassandra, DynamoDB, Data Lakehouses)
- Proficiency in data governance frameworks and metadata management tools (Collibra, Alation, Apache Atlas)
- Experience with BI & reporting tools (Power BI, Tableau, Looker, QlikView)
Educational Qualifications:
- Bachelor’s, Master’s, or PhD in Computer Science, Data Science, Information Technology, or a related field
- Certifications in AWS Certified Data Analytics, Google Professional Data Engineer, Snowflake Certified Data Engineer, or Microsoft Azure Data Engineer are preferred
Experience:
- 10+ years of hands-on experience in enterprise data warehousing, ETL development, and cloud-based analytics
- Proven track record of leading large-scale data warehouse initiatives and driving business intelligence transformation
- Experience in handling petabyte-scale datasets, distributed databases, and high-volume transactional systems
Key Focus Areas:
- Enterprise Data Warehouse Architecture & Optimization
- Cloud-Based Data Warehousing & Distributed Systems
- AI/ML & Predictive Analytics in Data Pipelines
- Data Governance, Compliance & Security
- Business Intelligence & Performance Analytics
Tools and Technologies:
- Databases: AWS Redshift, Google BigQuery, Snowflake, Azure Synapse, Teradata
- ETL & Data Pipeline Tools: Apache Airflow, Talend, Informatica, AWS Glue, dbt
- Big Data & Streaming Technologies: Hadoop, Spark, Kafka, Flink, Databricks
- BI & Reporting Tools: Power BI, Tableau, Looker, QlikView, ThoughtSpot
- Version Control & DevOps: Git, Terraform, Kubernetes, Docker, CI/CD Pipelines
- Monitoring & Performance Tools: SQL Profiler, SolarWinds, Prometheus, Grafana
Other Requirements:
- Exceptional analytical and problem-solving skills, with expertise in handling complex data architectures
- Strategic leadership and technical decision-making abilities for enterprise-level data projects
- Ability to communicate and present technical solutions to executive stakeholders and cross-functional teams
- Passion for innovation in AI-driven analytics, predictive modeling, and scalable data warehousing solutions