Data Engineer _Technology Lead

Key Responsibilities: Analyzes and solve problems using technical experience, judgment and precedents Provides informal guidance to new team members Explains complex information to others in straightforward situations 1. Data Engineering and Modelling: Design & Develop Scalable Data Pipelines: Leverage AWS technologies to design, develop, and manage end-to-end data pipelines with services like ETL, Kafka, DMS, Glue, Lambda, and Step Functions . Orchestrate Workflows: Use Apache Airflow to build, deploy, and manage automated workflows, ensuring smooth and efficient data processing and orchestration. Snowflake Data Warehouse: Design, implement, and maintain Snowflake data warehouses, ensuring optimal performance, scalability, and seamless data availability. Infrastructure Automation: Utilize Terraform and CloudFormation to automate cloud infrastructure provisioning, ensuring efficiency, scalability, and adherence to security best practices. Logical & Physical Data Models: Design and implement high-performance logical and physical data models using Star and Snowflake schemas that meet both technical and business requirements. Data Modeling Tools: Utilize Erwin or similar modeling tools to create, maintain, and optimize data models, ensuring they align with evolving business needs. Continuous Optimization: Actively monitor and improve data models to ensure they deliver the best performance, scalability, and security. 2. Collaboration, Communication, and Continuous Improvement: Cross-Functional Collaboration: Work closely with data scientists, analysts, and business stakeholders to gather requirements and deliver tailored data solutions that meet business objectives. Data Security Expertise: Provide guidance on data security best practices and ensure team members follow secure coding and data handling procedures. Innovation & Learning: Stay abreast of emerging trends in data engineering, cloud computing, and data security to recommend and implement innovative solutions. Optimization & Automation: Proactively identify opportunities to optimize system performance, enhance data security, and automate manual workflows. Key Skills & Expertise: Snowflake Data Warehousing: Hands-on experience with Snowflake, including performance tuning, role-based access controls, dynamic Masking, data sharing, encryption, and row/column-level security. Data Modeling: Expertise in physical and logical data modeling, specifically with Star and Snowflake schemas using tools like Erwin or similar . AWS Services Proficiency: In-depth knowledge of AWS services like ETL, DMS, Glue, Step Functions, Airflow, Lambda, CloudFormation, S3, IAM, EKS and Terraform . Programming & Scripting: Strong working knowledge of Python, R, Scala, PySpark and SQL (including stored procedures). DevOps & CI/CD: Solid understanding of CI/CD pipelines, DevOps principles, and infrastructure-as-code practices using tools like Terraform, JFrog, Jenkins and CloudFormation . Analytical & Troubleshooting Skills: Proven ability to solve complex data engineering issues and optimize data workflows. Excellent Communication: Strong interpersonal and communication skills, with the ability to work across teams and with stakeholders to drive data-centric projects. Qualifications & Experience: Bachelors degree in computer science, Engineering, or a related field. 7-8 years of experience designing and implementing large-scale Data Lake/Warehouse integrations with diverse data storage solutions. Certifications: AWS Certified Data Analytics - Specialty or AWS Certified Solutions Architect (preferred). Snowflake Advanced Architect and/or Snowflake Core Certification ( Required ).