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LPL Financial Global Capability Center in Hyderabad is seeking a Senior Data Engineer with 8+ years of experience to build scalable, secure data platforms. You will join Data & Platform Engineering to enable analytics, reporting, and downstream apps, leveraging AWS, Python, Spark, APIs, and containers.
The role emphasizes data quality, lineage, governance, and collaboration with architects, DevOps, and business stakeholders to deliver reliable data solutions at scale.
Job Description:
At LPL’s Global Capability Center, you'll find a collaborative culture where your voice matters, integrity guides every decision, and technology fuels progress. Your skills, talents, and ideas will redefine what's possible. LPL's success reflects its exceptional employees, who together pursue one noble purpose: empowering financial advisors to deliver personalized advice for all who need it. We’re proud to be expanding and reaching new heights in Hyderabad.
Join us as we create something extraordinary together.
We are seeking an accomplished Senior Data Engineer with 8+ years of experience who will be part of the Data & Platform Engineering organization responsible for building scalable, secure, and high-performance data platforms that support analytics, reporting, and downstream applications. This role is critical to enabling data-driven decision-making across the enterprise.
The Senior AWS Data Engineer requires strong hands‑on cloud data engineering expertise, deep knowledge of distributed and event‑driven architectures, and the ability to collaborate closely with application engineers, architects, and business stakeholders. This role plays a key part in designing, developing, and operating data pipelines that meet high standards for reliability, data quality, lineage, and governance.
The Senior AWS Data Engineer will contribute to building modern, cloud‑native data solutions leveraging AWS, Python, Spark, APIs, and containers. They will also partner with platform, architecture, and DevOps teams to ensure consistency, scalability, and operational excellence across data pipelines.
Design, develop, and maintain distributed data pipelines on AWS that ingest, process, and deliver data at scale. Implement both batch and event‑driven data processing patterns using AWS‑native services.
Build and support event‑driven data solutions using asynchronous, decoupled architectures to enable near real‑time processing and system scalability.
Provide hands‑on engineering using Python and Apache Spark to process large datasets efficiently. Apply best practices in distributed systems, performance optimization, and fault tolerance.
Ensure end‑to‑end data quality, implement validation and monitoring checks, establish data lineage, and manage orchestration workflows to ensure reliable and auditable data movement.
Design and develop RESTful and event‑driven APIs to expose data and data services to internal and external consumers.
Build and deploy data services using Docker containers and manage workloads on Kubernetes following cloud‑native and DevOps best practices.
Collaborate with data consumers, architects, platform teams, and business stakeholders to gather requirements, design scalable solutions, and deliver high‑quality outcomes.
Contribute to monitoring, logging, alerting, and incident response for data platforms. Ensure systems meet reliability, performance, and security standards.
We are looking for strong data engineers who can deliver reliable, scalable, and high‑quality data solutions. The ideal candidate thrives in a fast‑paced environment, is passionate about data engineering, and is comfortable working across teams to solve complex data problems.
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