Data Engineer

Location: Gurgaon, India (In-Office)
Department: Technology
Employment Type: Full-Time

About Sav

Sav is building the future of personal finance across the GCC. Our mission is to help people spend smarter, save effortlessly, and invest confidently through intuitive financial products. As a regulated fintech, trust and compliance are at the heart of everything we do.

We are looking for a Data Engineer to help build and scale the data infrastructure that powers Sav's products, analytics and decision-making. You will work across data architecture, pipelines, platforms and ML infrastructure, partnering closely with Product, Engineering, Analytics and other business teams.

About the Role

As a Data Engineer, you will be responsible for designing, building and maintaining scalable data systems that enable reliable access to high-quality data across the organisation.

You will play an important role in shaping Sav's Data Mesh architecture, building scalable data pipelines and enabling data infrastructure for analytics and machine learning use cases.

What You'll Do

• Design and build scalable Data Mesh architecture aligned with different business and product domains.
• Build and maintain robust data pipelines for ingestion, transformation and processing of large datasets.
• Design and optimise data warehouses and data models using Google BigQuery.
• Work with distributed data processing technologies such as Hadoop and Spark.
• Develop reliable ETL/ELT pipelines and ensure data is accurate, accessible and available to downstream teams.
• Build data infrastructure that supports ML and MLOps workflows, including data pipelines and model-related data requirements.
• Work closely with Product, Engineering, Analytics and ML teams to understand data requirements and build scalable solutions.
• Establish and improve data quality, validation, monitoring and governance processes.
• Optimise data pipelines and infrastructure for performance, scalability and cost efficiency.
• Troubleshoot data pipeline and infrastructure issues and ensure timely resolution of production problems.
• Contribute to data architecture, engineering standards and best practices across the organisation.

What We're Looking For

• 3-6 years of experience in Data Engineering or a closely related role.
• Strong experience designing and building data pipelines and data platforms.
• Hands-on experience with Google BigQuery and cloud-based data platforms.
• Strong understanding of Data Mesh architecture and domain-oriented data ownership.
• Experience working with Hadoop / Spark / distributed data processing.
• Strong proficiency in SQL and experience with Python for data engineering.
• Experience building and managing ETL/ELT pipelines.
• Understanding of data modelling, data warehousing and data architecture principles.
• Exposure to MLOps / ML pipelines and the data infrastructure required to support machine learning use cases.
• Strong understanding of data quality, governance, lineage and security.
• Ability to work with large datasets and build systems that are reliable, scalable and cost-efficient.
• Strong problem-solving skills and ability to work effectively with cross-functional teams.

Good to Have

• Experience with GCP ecosystem.
• Experience with Airflow, Kafka, dbt or similar data engineering tools.
• Experience with Kubernetes or containerised environments.
• Experience building data platforms for fintech, financial services or other regulated environments.
• Exposure to real-time data processing and streaming architectures.
• Experience working with feature stores or ML data platforms.