Data Engineering manager

				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.
			

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Panasonic Corporation of North America