Job Responsibilities:
- Interact closely with Business Leaders and Product Team to design, develop and implement an overall organizational data strategy that is in line with business processes. These data strategy includes data model designs (Conceptual, Logical and Physical), implementation and management of Data Warehouses / Data Lakes/ Data Lakehouse and Data Analytics Systems.
- Identifying data sources, both internal and external, and build complex data ingestion, data integration and ETL strategy that is aligned with organizational data/ analytical needs.
- Ensure data accuracy and reliability by performing data quality checks, reconciliation, and anomaly detection across multiple data sources, and work with the Tech team to resolve data gaps or inconsistencies.
- Setup and manage data access control matrix for all relevant stakeholders across all data assets within the enterprise systems / products.
- Collaborate with Business Leaders and Product Team to define and implement actionable metrics, KPIs and data visualizations dashboards and reporting capabilities keeping in view a self-service “Analytics-as-a-Service” model.
- Assess and recommend improvements to enhance / performance tune existing database structures, queries, ETL processes and data visualization processes to enable performance and overall scalability and availability.
- Produce and enforce Database Development standards, Data Governance standards, Master Data standards, Data Quality / Integrity standards and Data Standardization across the enterprise systems / products.
- Self-manage time / deliverables and work in ambiguous and frequently changing
environment - Ensure compliance to processes laid down by the department and the organization.
Essential Requirements:
- ETL & Data Integration — Design, Implement Scalable Data Ingestion, Transformation Strategies Based, Business Requirements; Build, Maintain Automated Data Pipelines.
- Data Warehouse & Data Modeling — Design Enterprise Data Warehouse Architectures, Dimensional Models, Lap Schemas to Support Analytical Workloads.
- Big Data & Distributed Processing — Work with Hadoop Ecosystem Technologies (Hadoop, Hive, Spark) To Process Large-scale Datasets, Optimize Distributed Queries, Support Scalable Data Processing Workflows.
- Python & Data Processing — Develop, Maintain Data Processing Scripts, Automate Workflows, Perform Data Cleaning, Transformation, Validation Using Python (pandas, Similar Libraries).
- Machine Learning & Data Visualization — Translate Business Requirements into Analytical Models, Dashboards, Visual Insights Using Ml Techniques, Visualization Tools
