Job Responsibilities:
- Design, develop, and maintain end to end data pipelines for ingesting, transforming, and delivering data from multiple source systems (databases, files, APIs, streaming platforms).
- Build and optimize ETL / ELT workflows using SQL, Python, and enterprise data integration tools.
- Ensure data pipelines are scalable, resilient, and performant to meet operational and analytical requirements.
Database & Data Platform Management
- Work hands on with RDBMS platforms such as Oracle, DB2, SQL Server, or PostgreSQL for data extraction, transformation, and performance tuning.
- Develop and optimize SQL queries, views, and stored procedures to support reporting and analytics use cases.
- Support data modelling activities (logical and physical) for analytics and reporting layers.
Data Quality, Governance & Operations
- Implement data validation, reconciliation, and monitoring to ensure data accuracy, completeness, and consistency.
- Support operational data activities, including incident investigation, root cause analysis, and remediation.
- Maintain clear documentation for data pipelines, schemas, and operational processes to support audits and knowledge transfer.
Collaboration & Stakeholder Engagement
- Collaborate with business users, product owners, and downstream teams to gather requirements and translate them into technical solutions.
- Work closely with Data Analysts, BI developers, and Data Scientists to enable dashboards, reports, and advanced analytics.
- Participate in Agile ceremonies and contribute to sprint planning, estimation, and delivery
Requirements:
- Bachelor’s degree in Computer Science, Engineering, Information Systems, or equivalent practical experience.
- Strong hands on experience with SQL and relational databases (Oracle, DB2, SQL Server, PostgreSQL).
- Experience building and supporting ETL / data pipelines in enterprise environments.
- Solid understanding of data modelling, data quality, and data lifecycle management.
- Ability to troubleshoot data issues and work in production / operational environments.
- Experience with Python for data processing or automation.
- Experience with data streaming technologies (e.g. Kafka, Spark, NiFi).
- Experience with BI and visualization tools such as Tableau, Qlik, or Power BI.