Kobie7mo ago

Sr. Data Engineer

Bengaluru, KarnatakaFull-Timesenior

Data EngineeringData EngineerDataData & AI

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Quick Summary

Overview

Join our India Tech Hub – Be among the first hires! Kobie, a 35-year veteran of the loyalty industry, a multi-year Forrester Leader, and USA Top Workplace is expanding its global footprint by establishing a Tech Hub in India.

Technical Tools

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Join our India Tech Hub – Be among the first hires!

Kobie, a 35-year veteran of the loyalty industry, a multi-year Forrester Leader, and USA Top Workplace is expanding its global footprint by establishing a Tech Hub in India. Kobie partners with global brands to build deep connections with their customers through personalized, data-driven loyalty experiences and has a mission of growing enterprise value through loyalty. The Tech Hub will serve as a Global Capabilities Center for a broad range of technology roles, and this is your chance to play a pivotal role in shaping our presence in India. Join us as we continue to lead in loyalty, delivering innovative customer experiences for some of the world’s most recognized brands while working alongside some of the best and brightest in loyalty.

About the team and what we will build together

We find actionable insights in our clients’ loyalty data that helps drive enterprise value. We create real-time dashboards to inform internal and external stakeholders.

As a Senior Data Engineer at Kobie, you will play a leading role within our data engineering team, partnering closely with the Manager of Data Engineering and the broader data organization. You will own the design and evolution of data pipelines and architectures that power Kobie’s products. Your deep expertise in data modeling, architecture, and scalable system design will help shape best practices across multiple Snowflake environments. You will guide Data Engineers through implementation while ensuring our data platform is secure, performant, and ready for growth.

Lead the design and implementation of scalable, reusable ETL/ELT frameworks and data pipelines across multiple client environments.

Define and enforce engineering best practices for data modeling, quality assurance, and performance optimization.

Mentor and support Data Engineers, conducting peer reviews and ensuring alignment with architectural standards.

Oversee the operational stability of production data pipelines, including monitoring, troubleshooting, and capacity planning.

Partner with data architects and product teams to design end-to-end data solutions aligned to business goals.

Conduct source data analysis and profiling to guide schema and model design decisions.

Implement and optimize data models including slowly changing dimensions, transactional, accumulating snapshot, and periodic snapshot fact tables.

Collaborate with platform engineers to evolve event-driven and streaming architectures supporting real-time analytics.

Drive automation initiatives for testing, auditing, and metadata capture to ensure transparency and reproducibility

5–7 years of Data Engineering experience, with at least 3 years operating in Snowflake.

Proven success designing and deploying large-scale data pipelines and dimensional models using modern cloud data platforms.

Deep understanding of Kimball and Data Vault methodologies, and how they integrate within Lakehouse and event-driven architectures.

Advanced proficiency with SQL and Python for pipeline development, data validation, and automation.

Experience leading data engineering projects through the full SDLC, from requirements through deployment and monitoring. Expertise in integrating batch and streaming data sources, including Kafka, APIs, and message queues.

Experience with orchestration and workflow management tools such as Airflow, Matillion or dbt.

Cloud experience with Azure and/or AWS, including cost optimization and data security best practices.

Strong communication and leadership skills to guide cross-functional teams and present technical concepts clearly to non-technical stakeholders.

Value-add: familiarity with vector and graph databases, Apache NiFi, and modern data sharing or clean-room technologies.