Senior Data Engineer

Senior Data Engineer (Product & Backend Focused, IC)

Location- Hyderabad/Ahmedabad

About the Role

Were hiring a hands-on Senior Data Engineer to build and operate the data platforms and services that power multiple customer-facing products. This is an individual contributor role focused on designing pragmatic, cost-efficient data architectures and exposing data through reliable, well-designed Python services.

Youll work in a matrixed data engineering organization, partnering with multiple product teams simultaneouslybalancing competing requirements, evolving schemas, and different performance and latency needs.

What Youll Do

• Design and build AWS-based data lake and analytics architectures using S3, Apache Iceberg, AWS Glue, Redshift, and DMS
• Own end-to-end data flows, from ingestion through storage to product-facing access layers
• Develop and maintain Python-based backend services and APIs that serve data to applications, internal tools, and downstream systems
• Partner with multiple product teams to understand requirements and deliver scalable, reusable data solutions
• Make deliberate tooling and architecture decisions with a strong focus on cost efficiency, performance, and operational simplicity
• Optimize table design, partitioning, indexing, and query patterns for real-world product workloads
• Ensure high availability, data correctness, and predictable performance for production systems
• Operate as a senior IC: writing code, reviewing PRs, debugging production issues, and improving platform reliability
• Document architectural decisions and trade-offs to support long-term maintainability

Required Qualifications

• 5+ years of hands-on experience in data engineering or backend engineering roles
• Strong experience designing and operating AWS data platforms, including:
• S3 with Apache Iceberg and Parquet
• AWS Glue (ETL and Data Catalog)
• Amazon Redshift
• AWS DMS
• Advanced Python skills, including building production-grade web services (e.g., FastAPI, Flask)
• Strong SQL skills and experience supporting low-latency and analytical workloads
• Proven experience designing systems with cost as a first-class constraint
• Experience supporting multiple products or domains simultaneously in a matrixed team environment
• Comfort owning systems in production, including monitoring, incident response, and performance tuning

Nice to Have

• Experience building data-backed APIs with authentication, authorization, and rate limiting
• Familiarity with infrastructure-as-code (Terraform, CDK, or CloudFormation)
• Exposure to ML-adjacent workloads (feature generation, offline training data)
• Experience with schema evolution and data versioning in product environments

What Success Looks Like

• Product teams can reliably and efficiently access the data they need via well-designed services and tables
• Data architectures scale across multiple products without unnecessary duplication
• Platform and product costs are continuously optimized as usage grows
• Data systems are observable, debuggable, and resilient in production
• Technical decisions are grounded in trade-offs and communicated clearly across teams