Role Overview

As aLead Data Engineer, you will play a critical role across the full data lifecycle—from business requirements gathering and data modeling to solution architecture, development, deployment, and production support. This is a hands-on technical leadership role requiring deepexpertisein building scalable, cloud-native data platforms usingSnowflake, Amazon Redshift, AWS, and modern data engineering tools.

You will lead the design and implementation of high-performance data pipelines, real-time streaming architectures, and analytics-ready data models while driving innovation throughGenAI-enabled data solutions, observability platforms, and modern transformation frameworks such asdbt. The ideal candidate is passionate about solving complex data challenges, mentoring engineering teams, and enabling enterprise-scale analytics and AI initiatives.

What You Will Do

• Design, develop, and implement scalable batch and real-time data pipelines usingSnowflake,Amazon Redshift, and AWS-native technologies 

• Lead end-to-end data engineering initiatives including data ingestion, transformation (ETL/ELT), data quality, and data delivery across enterprise platforms 

• Build andoptimizecloud-native data solutionsleveragingAWS services such asS3, Lambda, Glue, ECS, EMR, IAM, CloudWatch, and related services 

• Develop andmaintainmodern ELT transformation frameworks usingdbt(Data Build Tool)for modular, testable, and scalable data modeling 

• Implement and support real-time and CDC-based streaming architectures using tools such asKafka,STRIIM, and event-driven integration frameworks 

• Design scalable semantic and vector-based data architectures usingVector Databasesto support AI/ML and Retrieval-Augmented Generation (RAG) use cases 

• Collaborate with data architects, analysts, product owners, and business stakeholders to gather requirements and translate them into technical solutions 

• Drive best practices in data modeling, governance, metadata management, lineage, and performance optimization 

• Implement observability and monitoring solutions using tools such asDatadog, CloudWatch, and custom alerting frameworks to ensure platform reliability and operational excellence 

• Lead code reviews,establishengineering standards, and champion CI/CD and DevOps practices for data platforms 

• Troubleshoot and resolve complex production issues related to data pipelines, orchestration, and warehouse performance 

• Mentor junior and mid-level engineers, fostering a culture of technical excellence and continuous learning 

• Evaluate and adopt emerging technologies in cloud, AI/ML, and data engineering to drive innovation across the organization 

• Enable AI/ML initiatives by building trusted, scalable, and high-quality datasets for advanced analytics and GenAI applications 

• Partner with cross-functional teams to implement secure, compliant, andhighly availableenterprise data solutions 

What You Will Bring

• Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field 

• 12+ years of experience in Data Engineering, Data Warehousing, and Software Development 

• StrongexpertiseinSQLandPythonwith experience building enterprise-scale data solutions 

• Hands-on experience withSnowflakeandAmazon Redshiftin large-scale production environments 

• Strong experience with modern data transformation frameworks such asdbt

• Experience with orchestration and workflow tools such asApache Airflow

• Hands-on experience with streaming and CDC technologies such asKafka,STRIIM, or similar event-streaming platforms 

• Experience building and supporting observability frameworks usingDatadogor equivalent monitoring platforms 

• Familiarity withVector Databasesand AI/GenAI integration patterns for intelligent data applications 

• Exposure toGenAI tools, LLM-powered workflows, or AI-enabled analytics platforms is highly preferred 

• Strong understanding of dimensional and normalized data modeling techniques 

• Experience working with AWS cloud services includingS3, Lambda, Glue, IAM, ECS, CloudFormation, and related technologies 

• Knowledge of CI/CD implementation using tools such as GitHub Actions, Jenkins, Terraform, or similar platforms 

• Experience with data governance, data quality frameworks, and security best practices 

• Excellent problem-solving, analytical, and communication skills 

• Proven ability to lead technical initiatives and mentor engineering teams 

• Passion for innovation, continuous learning, and adopting modern data engineering practices