Lead Data Engineer

Design and deliver robust, production-grade data-intensive applications and services that power business-critical use cases at scale.
\n This role combines strong software engineering fundamentals with deep data engineering expertise and technical leadership.

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  • Bachelor’s degree in computer science, Computer Engineering, relevant technical field, or equivalent; Master’s degree preferred.
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Roles & Responsibility:

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• Operates effectively with minimal supervision, demonstrating strong ownership and accountability.
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• Translates high-level business requirements into well-designed, scalable, and robust systems and applications.
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• Exhibits exceptional data and software engineering expertise, drives architectural decisions and technology stack selection, establishes high-quality engineering standards, and ensures adherence through thorough code reviews and best practices.
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• Independently leads discussions with stakeholders, gathers and refines requirements, and develops deep domain expertise to deliver effective technical solutions.
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• Design end-to-end systems (ingestion, processing, storage, serving) with minimal supervision.
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• Build and maintain high-quality data products and pipelines on Spark and Databricks.
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• Design event-driven architectures using Kafka and related technologies for real-time data processing.
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• Build backend services and APIs that expose data capabilities reliably and securely.
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• Build and optimize data models for analytical workloads (for example, dimensional modeling, data vault)
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• Write clean, testable, and maintainable software in Scala (preferred) or Java.
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• Drive architecture and design discussions with engineers, architects, and stakeholders.
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• Coach and mentor junior engineers through code reviews, design sessions, and hands-on pairing.
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• Improve engineering standards across testing, observability, CI/CD, reliability, and documentation.
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• Deliver production-quality software with strong automated test coverage and clear quality gates.
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• Use design reviews and lightweight ADR-style documentation for major technical decisions.
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• Build observability into systems from day one (logs, metrics, tracing, alerts).
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• Deliver through CI/CD with safe release and rollback strategies.
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Skills

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• 8+ years of professional software engineering experience, including substantial backend and data platform work.
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• Strong programming background in Scala or Java (Scala preferred).
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• Proven experience building distributed data pipelines with Spark and Databricks.
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• Hands-on experience with Kafka in production systems.
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• Strong understanding of distributed systems concepts and failure handling.
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• Solid experience designing and developing APIs (REST and/or event-driven service interfaces).
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• Strong knowledge of relational databases (for example PostgreSQL), data modeling, and query optimization.
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• Demonstrated ability to design high-performance, concurrent systems.
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• Experience leading technical design and architecture decisions across teams.
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• Experience mentoring or coaching junior engineers.
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Good To Have:

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• Practical experience with Flink and advanced stream processing patterns.
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• Experience with cloud-native data platforms and infrastructure-as-code.
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• Experience with building & deploying applications on Kubernetes
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• Experience with data governance, security, and compliance in data platforms.
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• Familiarity with table formats and query engines in modern lakehouse ecosystems.
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• Experience with performance profiling and JVM tuning.
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Collaboration Scope:

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• Partner with product, platform, and domain teams to translate requirements into resilient technical solutions.
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• Work closely with architects and technical leadership to shape platform direction.
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• Support incident response and root-cause analysis for critical production issues.
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• Help define roadmaps that align business priorities with engineering sustainability.
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