Lead AWS Data Engineer

Role Summary

The Lead AWS Data Engineer provides technical leadership and hands‐on execution for enterprise data platforms hosted on Amazon Web Services (AWS). This role leads the design, migration, modernization, and operation of cloud‐native data architectures supporting mission‐critical financial, advisor payout, mobility, and corporate analytics platforms. The Lead AWS Data Engineer owns end‐to‐end technical decision‐making for AWS data platforms, including architecture, security, orchestration, and production readiness. The role requires deep expertise in AWS data services, Python and PySpark development, workflow orchestration, data lake design, infrastructure as code, and operational excellence, along with the ability to mentor engineers and partner effectively with infrastructure, IAM, and database teams. Key Responsibilitie sTechnical Leadership & Platform Ownershi pServe as the technical lead and design authority for AWS data engineering initiatives across multiple enterprise platforms .Own architectural decisions related to scalability, reliability, security, and cost optimization of AWS data platforms .Define and enforce engineering standards, coding patterns, and operational best practices for cloud data pipelines .Provide hands‐on technical guidance, design reviews, and code reviews for data engineers . AWS Data Platform Engineeri ngLead the design, development, and support of cloud‐native data pipelines using Amazon S3, AWS Glue (PySpark), MWAA (Apache Airflow), and AWS Step Function s.Drive on‐premises to AWS data platform migrations, including reverse engineering of legacy ETL workflows and re‐implementation using AWS‐native service s.Re‐architect legacy Oracle Data Integrator (ODI)–based ETL processes into scalable PySpark‐based Glue job s.Optimize Spark workloads for performance, memory usage, and cost efficiency in AWS Glue environment s. Data Lake, Iceberg & Architecture Des ignArchitect and implement enterprise AWS data lakes using Medallion architecture (Bronze, Silver, Gol d).Design and manage Apache Iceberg tables to support incremental processing, schema evolution, and efficient data lake operatio ns.Establish standardized ingestion, transformation, and consumption patterns across financial, mobility, and corporate datase ts.Ensure data quality, reconciliation, lineage, and auditability across all layers of the data platfo rm. Workflow Orchestration & Automa tionLead orchestration strategy using MWAA (Managed Workflows for Apache Airfl ow).Design and implement Airflow DAGs in Python to orchestrate end‐to‐end workflows, including Glue jobs, validations, and downstream dependenc ies.Implement scheduling, retry logic, monitoring, and failure handling to ensure resilient and scalable pipeli nes.Integrate orchestration workflows with AWS services such as S3, Glue, Athena, Iceberg‐based data lakes, and downstream syst ems. Security, Infrastructure & AWS Netwo rkingDrive implementation of AWS security best practices, including IAM role design, least‐privilege access, encryption using AWS KMS, and secrets manage ment.Lead configuration of AWS networking components such as VPC Endpoints (VPCE) to enable secure service‐to‐service communica tion.Manage infrastructure provisioning using Terraform, ensuring repeatable and auditable deployments across DEV, QA, and PROD environm ents.Coordinate with IAM, network, DevOps, and DBA teams to resolve access, firewall, and Oracle database connectivity challe nges. Production Readiness, Operations & S upportOwn production readiness for AWS data platforms, including configuration, secrets, access controls, and deployment pla nning.Act as the escalation point for complex production issues, performing root‐cause analysis and permanent fixes.Implement logging, metrics, and alerting using Amazon CloudWatch to meet enterprise SLAs and availability ta rgets.Support parallel‐run and hybrid architectures during migration phases to ensure business conti nuity. Automation, Compliance & Regulatory Ena blementDesign and oversee Python‐based automation solutions supporting operational efficiency and compliance initiatives (e.g., file retention and document proce ssing).Ensure pipeline designs meet regulatory, audit, and enterprise governance requirements, including traceability and controlled data ha ndling. Collaboration & Stakeholder En gagementPartner closely with data architects, DevOps teams, infrastructure teams, and business stakeholders to deliver AWS data solutions aligned with enterprise s business and platform requirements into scalable technical designs and executio n plans.Produce technical documentation and support knowledge transfer to enable long‐term platform sustain ability. Required Quali ficationsExperience with Apache Iceberg or similar data lake table to analytics or BI platforms (e.g., ThoughtSpot, Tableau).Exposure to Oracle databases or legacy ETL tools (e. g., ODI).Experience in financial services or regulated enterprise envi ronments.Familiarity with CI/CD practices for data engineering w orkloads.