Collaborates with Data Engineers, Data Scientists, Analytics teams, Cloud Architects, and business stakeholders to create scalable, high-performance data platforms that enable data-driven decision-making across the organization.

Key Responsibilities

Data Lake Architecture & Design

• Design and develop enterprise Data Lake and Lakehouse architectures.
• Define data storage structures for raw, curated, and consumption layers.
• Establish data partitioning, indexing, and lifecycle management strategies.
• Ensure scalability, reliability, and performance of data storage environments.
• Participate in data platform modernization and cloud transformation initiatives.

Data Ingestion & Integration

• Build and maintain data ingestion pipelines from various sources, including:
  • ERP systems
  • CRM platforms
  • IoT devices
  • Databases
  • APIs
  • Third-party applications
• Support both batch and real-time data ingestion processes.
• Implement change data capture (CDC) and streaming data solutions.
• Ensure seamless integration with enterprise ecosystems.

Data Lake Operations

• Manage and monitor data lake platforms and storage environments.
• Automate operational tasks and platform maintenance activities.
• Monitor data load performance, storage utilization, and processing efficiency.
• Troubleshoot and resolve platform-related issues.

Data Governance & Quality

• Implement data governance frameworks and best practices.
• Manage metadata, cataloging, lineage, and business glossary initiatives.
• Develop data quality monitoring and validation processes.
• Ensure compliance with organizational data standards and policies.
• Support Master Data Management (MDM) and data stewardship activities.

Security & Compliance

• Implement role-based access controls (RBAC) and data security policies.
• Ensure compliance with data privacy regulations and corporate governance standards.
• Monitor security vulnerabilities and implement remediation measures.
• Support security audits and risk assessments.

Cloud Data Platform Management

• Manage cloud-native data lake solutions.
• Optimize storage and compute resource utilization.
• Implement infrastructure automation and Infrastructure as Code (IaC).
• Support hybrid and multi-cloud data architectures.

Advanced Analytics Enablement

• Prepare and optimize datasets for analytics, reporting, and AI/ML projects.
• Enable self-service analytics through governed data access.
• Collaborate with Data Scientists and BI developers to improve data accessibility.
• Support implementation of Lakehouse and modern analytics architectures.

Documentation & Continuous Improvement

• Create and maintain architecture diagrams, standards, and operational procedures.
• Evaluate emerging technologies and recommend enhancements.
• Promote automation, operational excellence, and engineering best practices.
• Provide technical leadership and mentoring to junior team members.

Required Qualifications

Education

• Bachelor‘s degree in Computer Science, Information Technology, Data Engineering, Data Science, Software Engineering, or a related field.

Experience

• At least 5 years of experience in Data Engineering, Data Platform Administration, Data Warehousing, or Big Data solutions.
• 2+ years of hands-on experience with Data Lake or Lakehouse platforms.
• Experience managing cloud-based analytics and storage environments.
• Proven experience designing scalable enterprise data architectures.

Technical Skills

Data Lake & Lakehouse Platforms

• Azure Data Lake Storage (ADLS)
• Azure Microsoft Fabric OneLake
• Amazon S3 Data Lake
• Google Cloud Storage
• Databricks Lakehouse Platform
• Delta Lake
• Apache Iceberg
• Apache Hudi

Data Engineering

• ETL/ELT Frameworks
• Apache Spark
• Apache Kafka
• Azure Data Factory (ADF)
• Apache Airflow
• Databricks Workflows
• Data Pipeline Orchestration

Cloud Platforms

• Microsoft Azure
• Amazon Web Services (AWS)
• Google Cloud Platform (GCP)

Databases & Warehouses

• Snowflake
• Azure Synapse Analytics
• Amazon Redshift
• Google BigQuery
• Oracle
• SQL Server
• PostgreSQL

Programming Languages

• SQL
• Python
• Scala
• PySpark
• Shell Scripting

Data Governance & Security

• Data Catalog Solutions
• Microsoft Purview
• Collibra
• Metadata Management
• Data Lineage
• Data Classification
• Access Control & Encryption

DevOps & Automation

• Git
• GitHub / GitLab
• CI/CD Pipelines
• Docker
• Kubernetes
• Terraform
• Infrastructure as Code (IaC)

Soft Skills

• Strong analytical and problem-solving abilities.
• Excellent communication and stakeholder management skills.
• Ability to work cross-functionally with technical and business teams.
• Strong organizational and documentation capabilities.
• Proactive mindset focused on automation and continuous improvement.
• Ability to manage multiple priorities in fast-paced environments.