Senior data architect / lead data engineer

Role Overview

We are looking for a Senior Data Architect / Lead Data Engineer who can both design and build modern enterprise data solutions.

This is a hands-on architecture role. The ideal candidate can define architecture, model data, design pipelines, make technology decisions, troubleshoot production issues, and work directly with engineers and clients to deliver real solutions.

The primary focus is data architecture and data engineering. Experience with AI, RAG, semantic layers, knowledge graphs, or context engineering is highly preferred, but we are open to strong data professionals who are deeply curious and committed to becoming AI-native.

Key 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, Gen AI, 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.

Must-Have Experience

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.

Preferred Experience

Experience in any of the following areas is a strong plus:

RAG, Graph RAG, 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, Git Hub Copilot, Windsurf, Open AI, 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.

Mindset and Working Style

Hands-on builder, not a documentation-only architect. Comfortable working in an early-stage, fast-moving company. Strong ownership, urgency, and accountability. Willing to move between architecture, coding, client conversations, and delivery execution. Curious about AI and committed to continuous learning. Able to work independently with limited supervision. Comfortable challenging assumptions and proposing better technical approaches. Interested in helping build a company, not just completing assigned tasks.

Education

Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, Data Science, or a related field.

Relevant cloud, data platform, architecture, or AI certifications are beneficial but not mandatory.

Why Join Context66

Help build a modern Data, AI, and Enterprise Architecture company from the ground up. Work on real enterprise problems across data, architecture, and AI. Build modern data platforms, semantic layers, knowledge graphs, Graph RAG systems, and AI-ready foundations. Work closely with experienced technology leaders and enterprise clients. Influence technical standards, reusable assets, accelerators, and engineering culture. Grow into broader architecture, engineering leadership, and client-facing responsibilities as the company scales.

We are looking for strong data builders who combine architecture thinking, engineering discipline, curiosity, and commitment to customer outcomes.

Location:

Primary: Hyderabad, India. Other locations: Rest of India, Romania, Colombia, and Mexico.