Data Science / Data Engineering

The Role We're seeking an Architect (Senior/Specialist level) to serve as a senior technical authority for data architecture and analytics engineering on a modern Lakehouse platform across client engagements. In this role, you will design and own enterprise data platform architecture, lead complex data engineering initiatives, and ensure data capabilities are enterprise-grade, governed, and future-ready. You will bridge data engineering, analytics, and AI/ML—combining deep hands-on expertise in Python, SQL, PySpark, and modern lakehouse technologies with strong architectural judgment and stakeholder communication. Must-Have Skills (Non-Negotiable) • Lakehouse Platform Architecture: Hands-on architecture and engineering experience on a production-grade, enterprise-scale lakehouse platform. • Python: Strong professional proficiency for data engineering and pipeline development. • SQL: Advanced proficiency for data modeling, transformation, and performance tuning. Candidates without demonstrable hands-on experience in all three of the above will not be considered. What You'll Do • Own the architecture and design of enterprise lakehouse-based data platforms, including Delta Lake, medallion architecture, and unified analytics layers. • Serve as the senior technical authority on data platform design, providing guidance on architecture, tooling, modeling, and engineering standards. • Design and build ingestion, transformation, and orchestration pipelines using Python, SQL, PySpark, and workflow orchestration tools. • Architect data mesh and data product strategies, defining domain ownership, data contracts, and self-service data consumption. • Establish best practices across ingestion, transformation, modeling, quality, observability, governance, and data consumption. • Drive integration with AI/ML capabilities, including feature engineering pipelines, vector data infrastructure for RAG, and ML lifecycle management. • Lead complex data migration, platform modernization, and consolidation initiatives. • Evaluate emerging technologies such as Apache Iceberg, Delta Lake, Apache Hudi, Unity Catalog, Delta Live Tables, and Mosaic AI to inform architectural decisions. • Solve complex enterprise-scale data architecture challenges across multiple teams and platforms. • Drive cross-functional collaboration across data engineering, analytics, AI/ML, platform engineering, security, and product teams. • Mentor senior data engineers and analysts. • Engage with client stakeholders on data strategy and technical roadmaps. • Define and enforce governance, compliance (GDPR, HIPAA, SOC2), and responsible data management practices. • Drive FinOps maturity through compute optimization, storage lifecycle management, cluster governance, and cost optimization. What We're Looking For • 6–9 years of experience in data engineering, analytics engineering, or data architecture. • Mandatory hands-on expertise in Python, SQL, and enterprise lakehouse platforms. • Strong knowledge of the modern data stack: dbt, Airflow/Dagster, Spark, Kafka, Fivetran/Airbyte, and cloud-native data services. • Expertise in data modeling (Kimball, Data Vault, Activity Schema, OBT). • Experience with cloud platforms (AWS, Azure, or GCP). • Strong understanding of data governance, quality, lineage, cataloging, and compliance. • Experience supporting AI/ML workloads, including feature engineering, vector data, and model pipelines. • Proven technical leadership and stakeholder management. Preferred Qualifications • Experience architecting enterprise data platforms supporting LLMs, RAG pipelines, and Agentic AI. • Deep expertise in Delta Lake, Apache Iceberg, and Apache Hudi. • Experience implementing data mesh and federated data governance. • Experience with Kafka, Flink, Spark Structured Streaming. • Experience with Unity Catalog, Delta Live Tables, MLflow, Mosaic AI, and workflow orchestration. • Background in financial services, healthcare, SaaS, o