Data Specialist

Job description

As a Data Specialist, you will support the design and development of scalable backend data solutions, bridging modern data engineering with business analytics needs. The role focuses on building robust ETL pipelines, backend data workflows, and data models using Python and related technologies, while also supporting analytics and exploratory initiatives. You will work with structured and unstructured data sources, optimize data processing, and contribute to research, experimentation, and emerging AI/ML-driven use cases that improve operational performance and decision-making. Key Duties & Responsibilities 1. Support Data Engineering Assist in the end-to-end design, development, and scheduling of scalable ETL / ELT pipelines and backend data workflows using Python and related technologies. 2. Backend Development Build and maintain backend data processing logic, APIs, automation scripts, and integration workflows to support data movement and transformation. 3. Analyze Needs Collaborate with business and technical stakeholders to understand data requirements, assess current reporting/data workflows, and propose improved technical solutions. 4. Build Foundational Data Create datasets, attributes, metrics, and backend data models by writing optimized SQL queries, transformation logic, and validation routines. 5. Optimize Data Access Help organize and optimize data sources, database structures, and data extraction mechanisms to improve efficiency, reliability, and scalability. 6. Problem Solving & Troubleshooting Act as a technical resource for troubleshooting backend data issues, ETL failures, data inconsistencies, and performance bottlenecks. 7. Research & Innovation Participate in research, experimentation, and prototyping around modern data technologies, automation, and emerging engineering practices. 8. AI / ML Support Support AI / Machine Learning experimentation, proof-of-concepts, or data preparation tasks where required. 9. BI & Reporting Support Collaborate with reporting teams to provide clean, structured datasets for dashboards, analytics, and business reporting. Job Specifications Minimum Job Qualifications Requirement (Academic, training, languages, etc.) Education BSc in Computer Science, Software Engineering, Data Science, Engineering, or a relevant technical field Minimum Work Experience 1–5 years of relevant experience Strong technically capable junior candidates may also be considered Skills & Specifications Programming Languages Strong hands-on Python SQL PySpark (preferred) Data Engineering / ETL ETL / ELT pipeline development Data transformation and automation Backend workflow development API integration / data ingestion Databases MSSQL PostgreSQL MySQL IBM DB2 (good to have) Data Warehousing & Modeling Data Modeling Dimensional Modeling Star Schema Design Data Pipeline Optimization Cloud Platforms Azure / AWS / GCP exposure preferred Databricks exposure is a plus, but not mandatory AI / ML Exposure Basic AI / ML understanding Data preparation for AI/ML workflows Experimental / R&D mindset Reporting / Analytics Power BI / Tableau (supporting capability) Other Technologies Docker / containerization (good to have) Windows / Linux environments Networking fundamentals