Overview

Data Engineer Manager

Responsibilities

Data Engineering Leadership

• Lead and mentor a team of data engineers in developing and managing scalable, secure, and high-performance data pipelines.
• Define best practices for data ingestion, transformation, and processing in a Lakehouse architecture.
• Drive automation, performance tuning, and cost optimization in cloud data solutions.
  
  

Cloud Data Infrastructure & Processing

• Architect and manage AWS-based big data solutions (EMR, EKS, Glue, Redshift).
• Design and maintain Apache Airflow workflows for data orchestration.
• Optimize Spark and distributed data processing frameworks for large-scale workloads.
• Implement streaming solutions (Kafka, Kinesis, Flink) for real-time data processing.
  
  

AI/ML & Advanced Analytics

• Collaborate with Data Scientists and AI/ML teams to build and deploy machine learning models using AWS SageMaker.
• Support feature engineering, model training, and inference pipelines at scale.
• Enable AI-driven analytics by integrating structured and unstructured data sources.
  
  

Business Intelligence & Visualization

• Support BI and reporting teams with optimized data models for Amazon QuickSight and other visualization tools.
• Ensure efficient data aggregation and pre-processing for interactive dashboards and self-service analytics.
• Design, develop, and maintain middleware components that facilitate seamless communication between data platforms, applications, and analytics layers.
  
  

Master Data Management (MDM) & Governance

• Implement MDM strategies to ensure clean, consistent, and deduplicated data.
• Establish data governance policies for security, privacy, and compliance (GDPR, HIPAA, etc.).
• Ensure adherence to data quality frameworks across structured and unstructured datasets.
  
  

Collaboration & Strategy

• Partner with business teams, AI/ML teams, and analysts to deliver high-value data products.
• Define and maintain data architecture strategies aligned with business goals.
• Enable real-time and batch processing for analytics, reporting, and AI-driven insights.
  
  

Technical Expertise

• Extensive AWS experience with services such as EMR, EKS, Glue, Redshift, S3, Lambda, and SageMaker.
• Proficient in big data processing frameworks (e.g., Spark, Hive, Presto) and Lakehouse architectures.
• Skilled in designing and managing Apache Airflow workflows and other orchestration tools.
• Solid understanding of Master Data Management (MDM) and data governance best practices.
• Proficient with SQL & NoSQL databases (e.g., Redshift, DynamoDB, PostgreSQL, Elasticsearch).
• Middleware Development - Proven expertise in building middleware components like REST API that integrate data pipelines with applications, analytics platforms, and real-time systems.
• Hands-on experience with Gitlab CI/CD, Terraform, CFT, and Infrastructure-as-Code (IaC) methodologies.
• Familiarity with AI/ML pipelines, model deployment, and monitoring using SageMaker.
• Experience with data visualization tools, particularly AWS QuickSight, for business intelligence.
  
  

Qualifications

Experience with Lakehouse frameworks (Glue Catalog, Iceberg, Delta Lake).

Expertise in streaming data solutions (Kafka, Kinesis, Flink).

In-depth understanding of security best practices in AWS data architectures.

Demonstrated success in driving AI/ML initiatives from ideation to production.

Educational Qualification

• Bachelor’s degree or higher (UG+) in Computer Science, Data Engineering, Aerospace Engineering, or a related field.
• Advanced degrees (Master’s, PhD) in Data Science or AI/ML are a plus.
  
  

REQ-145778