Traditional Data Engineer at Weekday AI

Bengaluru, karnataka, India -

Full Time

Start Date

Immediate

Expiry Date

17 Apr, 26

Salary

0.0

Posted On

17 Jan, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Engineering, PL/SQL, Python, Data Pipelines, Data Quality, Data Warehousing, ETL, Data Modeling, Query Tuning, Data Governance, Automation, Problem Solving, Stakeholder Collaboration, Technical Documentation, Performance Optimization, Mentorship

Industry

technology;Information and Internet

Description

This role is for one of the Weekday's clients Min Experience: 7 years Location: Bengaluru JobType: full-time This role is designed for an experienced Traditional Data Engineer who thrives on building, maintaining, and optimizing reliable data systems that power business intelligence and analytics. Based in Bengaluru, this full-time position focuses on designing and managing structured data pipelines using proven data engineering practices, with strong emphasis on PL/SQL and Python. You will play a critical role in ensuring data accuracy, performance, and availability across enterprise systems, supporting reporting, analytics, and downstream applications. The role requires deep technical expertise, a strong understanding of relational databases, and the ability to work closely with stakeholders to translate data requirements into scalable solutions. Key Responsibilities Design, develop, and maintain robust data pipelines to extract, transform, and load data from multiple source systems into enterprise data platforms Build and optimize complex PL/SQL procedures, functions, packages, and triggers to support large-scale data processing and reporting needs Develop Python-based scripts and frameworks for data ingestion, validation, transformation, and automation Ensure data quality, integrity, and consistency across datasets through validations, reconciliations, and monitoring processes Collaborate with business analysts, reporting teams, and application owners to understand data requirements and deliver reliable datasets Optimize database performance through query tuning, indexing strategies, and efficient data modeling techniques Support data warehouse and data mart development, including schema design, fact and dimension modeling, and historical data management Troubleshoot and resolve data-related issues in production environments, ensuring minimal downtime and accurate root-cause analysis Implement best practices for version control, documentation, and deployment of data engineering solutions Adhere to data governance, security, and compliance standards while handling sensitive and business-critical information Participate in system upgrades, data migrations, and performance improvement initiatives Provide technical guidance and mentorship to junior data engineers and contribute to continuous process improvements What Makes You a Great Fit A minimum of 7 years of hands-on experience in data engineering or related roles, with strong exposure to traditional data platforms Deep expertise in PL/SQL, including performance tuning, complex transformations, and large-scale data processing Strong proficiency in Python for data engineering tasks, automation, and scripting Solid understanding of relational databases, data modeling concepts, and ETL/ELT processes Experience working with enterprise data warehouses and structured datasets Strong analytical and problem-solving skills with the ability to handle complex data challenges Ability to work independently while collaborating effectively with cross-functional teams High attention to detail, commitment to data accuracy, and ownership of end-to-end data workflows Clear communication skills to explain technical concepts to both technical and non-technical stakeholders

Responsibilities

Design, develop, and maintain robust data pipelines to extract, transform, and load data from multiple source systems into enterprise data platforms. Collaborate with business analysts and reporting teams to understand data requirements and deliver reliable datasets.