Description

You will define enterprise data architecture principles, target-state blueprints, and reference architectures for data platforms, integration, governance, and consumption patterns.

This role is on-site.

Responsibilities

• Define data modeling standards, 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 batch and streaming data pipelines.
• Lead client discovery and architecture workshops to translate business goals into target-state architecture, migration strategies, and phased roadmaps.
• Embed AI into the data engineering lifecycle by defining standards and guardrails for AI-assisted development and establishing measurable quality gates.

Required Skills

• 10-12+ years of experience in data engineering, data architecture, or platform engineering.
• Proficiency in Spark, SQL, Python, ELT, Kafka, ADF, AWS, and Databricks.
• Experience with CI/CD practices and Terraform for infrastructure as code.
• 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.
• Significant experience in client-facing consulting, solution architecture, or program delivery leadership.

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