Job Summary

We are seeking an experienced Data Engineering Architect / Lead Data Engineer to design, build, and optimize scalable data platforms and pipelines. The ideal candidate will have extensive experience in ETL/ELT development, cloud-based data engineering, real-time and batch data processing, and modern data warehouse architectures. This role requires strong expertise in designing end-to-end data solutions that are scalable, secure, and aligned with enterprise standards.

Required Skills & Qualifications

Advanced expertise in designing and developing ETL/ELT pipelines.
Strong experience with batch data processing and near real-time/streaming data pipelines.
Hands-on experience working with structured and semi-structured data.
Strong knowledge of:
- Incremental data loading
- Change Data Capture (CDC)
- Pipeline orchestration and dependency management
Strong programming skills in Python (preferred), Scala, or Java.
Experience optimizing large-scale data processing workloads for performance and cost.
Solid understanding of data modeling concepts:
- Star Schema
- Snowflake Schema
- Normalized and denormalized data models
Hands-on experience with at least one major cloud platform:
- Microsoft Azure
- Amazon Web Services (AWS)
- Google Cloud Platform (GCP)
Strong experience with modern data warehouses such as:
- Snowflake
- Azure Synapse
- Google BigQuery
- Amazon Redshift

Key Responsibilities

Data Architecture & Solution Design

Design end-to-end data engineering architectures for enterprise-scale solutions.
Develop scalable architectures for:
- Data Lakes and Lakehouse platforms
- Enterprise Data Warehouses
- Streaming and real-time data processing systems
Ensure solutions align with enterprise architecture, security, governance, and compliance standards.
Review and approve technical designs and implementation strategies.

Data Pipeline Development & Management

Lead the design and development of scalable ETL/ELT pipelines.
Build and manage data ingestion pipelines for both batch and real-time data.
Process structured and semi-structured data efficiently.
Optimize data pipelines for performance, reliability, scalability, and cost.
Manage schema evolution, metadata, and pipeline dependencies.

Data Quality, Reliability & Operations

Establish and enforce data quality standards and validation frameworks.
Implement monitoring, alerting, logging, and observability for data pipelines.
Perform root cause analysis and resolve data-related production issues.
Drive operational excellence by improving system stability and reliability.

DevOps / DataOps

Build and maintain CI/CD pipelines for data engineering workloads.
Automate testing, deployment, and rollback processes.
Improve platform reliability and deployment efficiency through automation and DevOps best practices.

Preferred Qualifications

Experience with modern DataOps and CI/CD practices.
Knowledge of data governance, security, and compliance frameworks.
Experience designing enterprise-scale cloud-native data platforms.
Strong analytical, troubleshooting, and communication skills.

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