Description

You will define enterprise data architecture principles, target-state blueprints, and reference architectures for data platforms, integration, governance, and consumption patterns. Partner with business and technology stakeholders to translate strategic objectives into scalable data products and platform capabilities. Lead client discovery and architecture workshops to understand business goals, current-state landscapes, constraints, and value cases; translate these into target-state architecture, migration strategies, and phased roadmaps. Serve as a trusted advisor to client executives and senior stakeholders; drive architecture governance (design authorities), decision logs, and risk management across complex programs. Lead architecture for cloud/hybrid data solutions including data lakes/lakehouses, warehouses, streaming, and real-time analytics.

Responsibilities

• Establish data modeling standards (conceptual/logical/physical), metadata strategy, master/reference data patterns, and semantic layers.
• Drive data governance, data quality, privacy, and security-by-design in alignment with enterprise policies and regulatory requirements.
• Architect and guide implementation of ingestion, transformation, orchestration, and CI/CD for data pipelines (batch and streaming).
• Embed AI into the data engineering lifecycle by defining standards and guardrails for AI-assisted development and establishing measurable quality gates.
• Lead and coordinate delivery across client and partner teams ensuring scope clarity, dependency management, and adherence to agreed architecture and quality standards.

Required Skills

• 10-12+ years of experience in data engineering, data architecture, or platform engineering.
• Significant experience in client-facing consulting, solution architecture, or program delivery leadership.
• Proven experience designing and implementing enterprise-scale data platforms (lake/lakehouse/warehouse) on cloud and/or hybrid environments.
• Strong foundation in data modeling, integration patterns, distributed processing, and performance tuning.
• Hands-on understanding of governance disciplines: data quality, catalog/metadata, lineage, privacy, retention, and access controls.
• Proficiency in Spark, SQL, Python, ELT, Kafka, ADF, AWS, and Databricks.
• Experience with CI/CD practices and Terraform for infrastructure as code.
• Ability to apply data engineering skills to AI-ready data products (training/inference readiness, feature readiness, and observability).

Preferred Skills

• Working knowledge of AI-enabled engineering practices and/or MLOps fundamentals (model lifecycle, evaluation, monitoring).
• Excellent consulting communication skills: facilitate workshops, synthesize ambiguity into options/trade-offs, and present recommendations.

Key Skills

Education

Any Gradute