Technical Architect
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Job Summary
SQUARESHIFT TECHNOLOGIES
Technical Architect
Location: Chennai India (Hybrid) Practice: Cloud Data & AI Reports to: Director of Engineering
About SquareShift
SquareShift is a Google Cloud Premier Partner Looker Delivery-Verified Partner and Elastic Professional Services partner. We design and build production cloud data and AI/ML systems for ISVs SaaS companies and enterprises across the US and India.
Our work is technically dense multi-tenant data platforms on BigQuery semantic layer migrations to Looker observability and SIEM on Elasticsearch and a growing portfolio of internal agentic AI products. Architects at SquareShift are not slide-deck architects. They write reference implementations pair with engineers and own technical decisions all the way to production.
The Role
We are hiring a Technical Architect to lead the design of our most complex engagements and to set the technical direction for one of our practice areas Cloud & Data Platforms AI/ML & Agentic Systems or Observability & Search.
You will be the senior technical voice on 23 concurrent engagements: scoping with clients in pre-sales owning the architecture from RFC to go-live and resolving the hard problems that engineers cannot resolve alone. You will also help shape our reusable accelerators (T2L M2L P2L C2L for BI migrations; agent frameworks for Conversational BI and AIResolveX) so that the second engagement of a kind is faster and cleaner than the first.
What You Will Own
Architecture & Design
Lead end-to-end architecture for client engagements: data platforms on GCP (BigQuery Cloud Run GKE Dataflow Pub/Sub) semantic layers in LookML agent systems on Vertex AI / Google ADK or Elasticsearch/OpenSearch deployments at scale.
Make the deployment-platform call Cloud Run vs GKE vs serverless based on workload shape latency cost and the clients operational maturity. Document the trade-off dont default to one.
Write architecture decision records that engineers can build from and clients can defend internally covering trade-offs alternatives considered and operational implications.
Own non-functional requirements: latency targets cost envelopes security posture (HIPAA/GDPR/PII handling where relevant) observability and disaster recovery.
Design for multi-tenancy scale and the realities of consulting handover code that the client team can run after we leave.
Hands-On Build
Write reference implementations and the hardest 1020% of code on each engagement the pieces that unblock the rest of the team.
Run spikes and POCs to de-risk new patterns before they hit a delivery plan.
Pair with senior engineers on debugging performance tuning and production go-lives.
Pre-Sales & Technical Strategy
Partner with Pre-Sales on scoping calls technical workshops and SOW reviews. You are the engineer in the room when a prospects CTO is asking hard questions.
Build and maintain reusable accelerators reference architectures and migration playbooks within your practice area.
Represent SquareShift externally partner technical reviews with Google Cloud and Elastic blog posts conference talks and the occasional joint webinar.
What You Need to Have
10 years in software/data engineering with at least 3 years in an architect principal or staff-level role owning systems end-to-end.
Deep current expertise in at least one of our practice areas:
Cloud & Data BigQuery at scale Looker/LookML modelling GCP services (Cloud Run GKE/Kubernetes Dataflow Pub/Sub Vertex AI) Helm and container deployment patterns and modern data stack tooling (dbt Airflow).
AI/ML & Agentic Systems production LLM applications RAG architectures agent orchestration (ADK LangGraph MCP) evaluation frameworks and cost/latency engineering for LLM workloads.
Observability & Search Elasticsearch/OpenSearch cluster design ingest pipelines SIEM/security analytics and capacity planning at multi-TB ingestion scales.
Production track record. You can point to systems running today with traffic that you designed and helped build.
Strong design communication. Your RFCs and diagrams are clear enough that a senior client architect can disagree with you on substance not on what you meant.
Pragmatism. You optimise for the outcome not the most novel framework. You know when boring infrastructure is the right answer.
Bonus Points For
Hands-on experience with Anthropic Claude Google ADK OpenRouter or similar agent platforms in production.
Background in BI migrations to Looker from Tableau MicroStrategy Cognos or Power BI.
Elastic certifications Google Cloud Professional certifications or open-source contributions in our ecosystem.
Prior experience in a consulting or partner-led delivery model you understand the rhythm of pre-sales SOW delivery and handover.
Why This Role Is Worth Your Time
Architecture with teeth. Our architects ship code not just decks. If you have been quietly frustrated by architect roles that drift from production this is a course correction.
Variety with depth. Multiple engagements per year across genuinely different problem spaces without the chaos of a body-shop model.
Direct partner access. Real working relationships with Google Cloud and Elastic engineering teams; you will ship things that show up in case studies and partner reviews.
Influence on the AI roadmap. Our agentic product portfolio (Conversational BI AIResolveX Agentic QA) is being designed now. Architects here help decide where it goes.
Required Skills:
Required Skills & Experience Core Technical Skills Strong proficiency in Python SQL PySpark. Hands-on expertise with Kafka Kafka Connect Debezium Airflow Databricks. Deep experience with BigQuery Snowflake MySQL Postgres MongoDB. Solid understanding of vector data stores and search indexing. Knowledge of GCP services like Big Query Cloud Functions Cloud Run Data Flow Data Proc Data Stream etc.. Good to have Certifications: GCP Professional Data Engineer Elastic Certified Engineer AI Gemini Enterprise Vertex AI Agent Builder ADK Non-Technical & Leadership Skills Communication: Exceptional verbal and written communication skills with the ability to articulate complex technical concepts to both technical and non-technical audiences. Mentorship & Coaching: Proven experience in mentoring junior and mid-level engineers fostering a culture of continuous learning and growth. Problem-Solving: Strong analytical and debugging skills with a proactive approach to identifying and resolving technical roadblocks. Ownership & Accountability: Demonstrates a high level of responsibility for project outcomes system reliability and code quality. Agile Proficiency: Deep understanding and practical experience with Agile methodologies (Scrum/Kanban). Stakeholder Management: Ability to effectively manage expectations and build consensus across different teams. Qualifications Bachelors or Masters degree in Computer Science Engineering or a related field (or equivalent practical experience). Typically 7 years of progressive experience in data engineering with 2 years in a technical leadership or lead engineer role.
