Senior Data Architect / Lead Data Engineer

Context66 is seeking a Senior Data Architect / Lead Data Engineer who can design and build modern enterprise data solutions. The role requires hands-on architecture skills to define architecture, model data, and design pipelines while collaborating with engineers and clients to deliver effective solutions.

Responsibilities:

• Design and build modern data platforms across warehouses, lakes, lakehouses, and cloud data platforms
• Develop scalable ingestion, transformation, orchestration, and consumption pipelines
• Design batch, micro-batch, CDC, API-based, and event-driven integration patterns
• Build canonical, logical, physical, and semantic data models
• Implement data quality, metadata, lineage, governance, access control, and observability patterns
• Design governed data products with clear ownership, contracts, SLAs, and quality expectations
• Support analytics, reporting, machine learning, GenAI, and AI-agent use cases with trusted data foundations
• Work hands-on with SQL, Python, dbt, Snowflake, Databricks, Spark, AWS, Azure, or equivalent platforms
• Create reusable frameworks, templates, standards, and delivery accelerators
• Perform architecture reviews, code reviews, performance tuning, and technical troubleshooting
• Partner with clients and internal teams to translate business problems into scalable technical solutions
• Mentor engineers while remaining hands-on when needed

Requirements:

• 8 to 15+ years of experience in data architecture, data engineering, integration, analytics, or cloud data platforms
• Strong hands-on SQL and data engineering experience
• Practical experience with Snowflake, Databricks, Spark, dbt, Python, AWS, Azure, GCP, or similar technologies
• Strong understanding of data warehousing, data lake, lakehouse, and modern cloud data architecture
• Experience designing production pipelines using ETL, ELT, CDC, APIs, orchestration, and batch processing
• Strong data modeling skills, including canonical, logical, physical, and dimensional modeling
• Ability to design restartable, auditable, and production-grade data pipelines
• Good understanding of data quality, metadata, lineage, governance, security, and access controls
• Strong problem-solving and debugging skills
• Ability to explain technical decisions clearly to engineers, architects, and business stakeholders
• Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, Data Science, or a related field
• Experience in any of the following areas is a strong plus: RAG, GraphRAG, LLMs, AI agents, or enterprise AI platforms
• Knowledge graphs, graph databases, ontologies, or semantic modeling
• Enterprise semantic layers, metric definitions, business glossaries, or data catalogs
• Vector databases, hybrid search, metadata-driven retrieval, or AI grounding
• AI-assisted engineering using tools such as Claude Code, Cursor, GitHub Copilot, Windsurf, OpenAI, Gemini, or similar tools
• Data products, data mesh, data fabric, active metadata, or policy-driven governance
• MDM, golden records, survivorship rules, and cross-system entity resolution