Data Platform Engineer

• 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

Required Skills & 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.

• Knowledge of cloud or hybrid data platforms and orchestration tools.

• Knowledge of Agile / DevOps practices and CI/CD for data pipelines.