Data Lake Engineer

Overview

We are looking for a skilled Data Lake Engineer to design, build, and maintain scalable, secure, and high-performance data lake platforms that enable analytics, reporting, and data-driven decision-making. The role involves building robust data pipelines, optimizing storage and processing, and ensuring data reliability, governance, and cost efficiency.

Key Responsibilities

• Design and implement scalable Data Lake architecture for structured, semi-structured, and unstructured data
• Build and maintain reliable ETL/ELT pipelines for batch and real-time ingestion
• Optimize data storage, partitioning, and performance for analytics workloads
• Implement data quality, validation, and governance standards
• Collaborate with Data Engineering, Analytics, Product, and DevOps teams
• Enable self-service analytics through curated datasets and reusable components
• Monitor system performance, reliability, and cost optimization
• Automate deployments and workflows using CI/CD and Infrastructure as Code
• Ensure security, compliance, and access controls across the platform
• Troubleshoot data issues and drive continuous improvements
  
  

• Strong experience with Python/Scala/SQL
• Hands-on experience with Data Lake technologies (e.g., object storage, distributed compute engines)
• Experience building ETL/ELT pipelines and orchestration frameworks
• Knowledge of data modeling and schema design
• Familiarity with APIs, microservices, and streaming concepts
• Understanding of performance tuning and cost optimization
• Strong problem-solving and system design skills
  
  

• Experience with real-time/streaming pipelines
• Exposure to DevOps/CloudOps practices
• Data governance and metadata management knowledge
• Observability/monitoring tools experience
• Domain knowledge in analytics/reporting platforms
  
  

• 3–5 years (Engineer)
• 6–10+ years (Senior/Architect)
  
  

• Ownership mindset with end-to-end accountability
• Quality-first approach (first-time-right delivery)
• Strong collaboration across teams
• Customer-centric thinking
• Focus on reliability, scalability, and cost efficiency