Job Description:

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.
Knowledge of cloud or hybrid data platforms and orchestration tools.
Knowledge of Agile / DevOps practices and CI/CD for data pipelines.
Minimum 2 years working experience.
3 must have skillset: SQL, Python, Tableau.

Data Platform Engineer (CAT 1)