We are looking for a highly skilled Lead Data Engineer to design, build, and optimize scalable cloud-based data platforms and processing pipelines for enterprise-grade applications. This role is ideal for someone who enjoys solving large-scale data engineering challenges and has deep expertise in Google Cloud Platform (GCP), Apache Beam/Dataflow, Java, and BigQuery.
As a Lead Data Engineer, you will play a critical role in building robust batch and real-time data processing systems that enable analytics, reporting, and data-driven decision-making across the organization. You will work closely with architects, analysts, DevOps teams, and business stakeholders to develop reliable, scalable, and high-performance data solutions.
The ideal candidate is passionate about modern cloud data architectures, distributed systems, and building efficient ETL/ELT pipelines capable of handling large-scale enterprise workloads. This role requires strong technical expertise, problem-solving ability, and the capability to drive engineering best practices across data platforms.
Key Responsibilities
Design and develop scalable ETL/ELT pipelines using Google Cloud Platform services
Build and maintain real-time and batch data processing pipelines using Apache Beam and Google Dataflow
Develop backend processing components and data transformation services using Java
Work extensively with BigQuery for data warehousing, analytics, querying, and performance optimization
Integrate data from multiple sources including APIs, relational databases, streaming systems, and cloud platforms
Build reliable and scalable streaming data pipelines using technologies such as Kafka and cloud-native services
Optimize pipeline performance, scalability, reliability, and cloud infrastructure costs
Ensure high standards of data quality, governance, monitoring, security, and operational excellence
Collaborate with cross-functional teams including Architects, Analysts, DevOps, and business stakeholders to deliver data-driven solutions
Troubleshoot production issues, perform root-cause analysis, and implement long-term scalable fixes
Implement CI/CD practices, version control workflows, and automated deployment pipelines for data engineering solutions
Design and maintain scalable data models, schemas, and warehouse structures
Participate in architecture discussions and contribute to improving overall cloud data platform capabilities
Drive best practices in distributed data processing, pipeline optimization, and cloud-native engineering
What Makes You a Great Fit
Strong hands-on experience with Google Cloud Platform (GCP) services
Deep expertise in Apache Beam and Google Dataflow for large-scale data processing
Strong programming skills in Java with experience building backend processing systems
Hands-on experience with BigQuery and cloud-based data warehousing solutions
Experience building and maintaining batch and real-time streaming data pipelines
Strong understanding of SQL, data modeling, and distributed data processing concepts
Experience working with Kafka or similar streaming technologies
Familiarity with CI/CD pipelines, Git workflows, and Agile development methodologies
Strong analytical, troubleshooting, and debugging capabilities
Understanding of scalable cloud architecture, performance optimization, and reliability engineering
Ability to work collaboratively with cross-functional engineering and business teams
Experience handling enterprise-scale datasets and complex integration requirements
Strong communication skills with the ability to translate technical solutions into business impact
Passion for building modern cloud-native data platforms and solving large-scale engineering challenges
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