WAM- AWS-Data-Engineer-Manager

• Serve as the

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.

• Lead the design, development, and support of cloud native data pipelines using

• Drive

including reverse engineering of legacy ETL workflows and re implementation using AWS native services.

• Re architect legacy

• Optimize Spark workloads for performance, memory usage, and cost efficiency in AWS Glue environments.

• Architect and implement enterprise

• Design and manage

to support incremental processing, schema evolution, and efficient data lake operations.

• Establish standardized ingestion, transformation, and consumption patterns across financial, mobility, and corporate datasets.

• Ensure data quality, reconciliation, lineage, and auditability across all layers of the data platform.

• Lead orchestration strategy using

• Design and implement Airflow DAGs in Python to orchestrate end to end workflows, including Glue jobs, validations, and downstream dependencies.

• Implement scheduling, retry logic, monitoring, and failure handling to ensure resilient and scalable pipelines.

• Integrate orchestration workflows with AWS services such as

• Drive implementation of AWS security best practices, including

• Lead configuration of AWS networking components such as

to enable secure service to service communication.

• Manage infrastructure provisioning using

, ensuring repeatable and auditable deployments across DEV, QA, and PROD environments.

• Coordinate with IAM, network, DevOps, and DBA teams to resolve access, firewall, and Oracle database connectivity challenges.

• Own

for AWS data platforms, including configuration, secrets, access controls, and deployment planning.

• Act as the escalation point for complex production issues, performing root cause analysis and permanent fixes.

• Implement logging, metrics, and alerting using

to meet enterprise SLAs and availability targets.

• Support parallel run and hybrid architectures during migration phases to ensure business continuity.

• Design and oversee

supporting operational efficiency and compliance initiatives (e.g., file retention and document processing).

• Ensure pipeline designs meet

• Partner closely with data architects, DevOps teams, infrastructure teams, and business stakeholders to deliver AWS data solutions aligned with enterprise strategy.

• Translate business and platform requirements into scalable technical designs and execution plans.

• Produce technical documentation and support knowledge transfer to enable long term platform sustainability.

• Strong preference for candidates with experience in:

• Wealth Management

• Asset Management

• Capital Markets

• Investment Banking

• Financial Services

• Exposure to:

• Portfolio & Investment Management

• Securities & Holdings Data

• Trade & Transaction Processing

• Risk & Compliance Reporting

• Market & Reference Data

• Client Reporting Platforms

• Regulatory Reporting

Preferred skills:

• AWS Data Engineering Services (Glue, EMR, Step Functions, S3, CloudWatch)

• Apache Airflow

• Iceberg

• Data Architect

• DWH/ETL

• Python

• Pyspark

• Medallion Architecture

• SQL

• IaC (Devops, Terraform)

• Data Modelling

• WAM Domain Knowledge

• Github Copilot, Claude

• Exposure 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 environments.

• Familiarity with CI/CD practices for data engineering workloads.

• AWS Certification (Data Analytics, Solutions Architect, or Developer Associate).

• Bachelor's degree in computer science, Engineering, or related field (or equivalent practical experience).

•

in Data Engineering with strong AWS exposure.

• Extensive hands on experience designing and leading

• Proven experience leading or owning

• Advanced proficiency in

for data processing, automation, and orchestration.

• Strong experience with

and related services.

• Experience with

.

• Strong understanding of data engineering principles including ETL design, data modelling, and pipeline optimization.

• Demonstrated experience supporting

• Good understanding of AI technologies, agentic systems, and tools such as Microsoft Copilot and Claude, with the ability to leverage AI-driven capabilities for solution development and delivery.