Engineering Manager
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Job Summary
SQUARESHIFT TECHNOLOGIES
Engineering Manager
Location: Chennai India (Hybrid) Team: Engineering & Delivery Reports to: Director of Engineering
About SquareShift
SquareShift is a specialized consulting firm focused on cloud modernization data engineering AI/ML and observability. We are a Google Cloud Premier Partner a Looker Delivery-Verified Partner and an Elastic Professional Services partner combining deep platform expertise with senior-led delivery for ISVs SaaS companies and enterprises across the US and India.
We are not a staffing shop. We are a boutique that builds production data platforms agentic AI products and observability systems that go live and stay live. Our engineers work directly with client architects and CTOs.
The Role
We are looking for an Engineering Manager to lead a multi-pod team of senior engineers across our cloud data and AI/ML practices. You will own delivery quality engineering health and people growth across two to four concurrent client engagements typically a mix of GCP data platform builds Looker/BI migrations Elasticsearch/observability work and our internal agentic AI products (Conversational BI AIResolveX Agentic QA).
This is a hands-on leadership role. You will not write production code every day but you will read PRs sit in architecture reviews unblock debugging sessions and coach senior engineers through hard technical decisions. You are the person clients escalate to when something is on fire and the person engineers go to when they need air cover.
What You Will Own
Delivery
Lead 1525 engineers across 24 concurrent engagements balancing client commitments with engineer growth and bench utilization.
Run weekly delivery reviews using our engineering cockpit metrics across Delivery Quality and Health pillars (velocity escape rate on-call load MTTR engagement scores).
Own SOW-to-go-live execution: scoping inputs milestone plans risk registers change orders and stakeholder communication with client engineering leadership.
Partner with Pre-Sales on technical scoping for prospective deals sizing staffing models and risk flags before SOWs go out.
Quality
Set and enforce the engineering bar across pods: code review standards testing coverage observability defaults and production readiness checklists.
Sign off on architecture decisions for net-new builds and migrations (BigQuery data platforms Looker/LookML semantic models Elasticsearch/OpenSearch clusters Vertex AI agent deployments).
Drive root-cause analysis for client incidents and internal escapes; ensure learnings flow back into our playbooks and accelerators (T2L M2L P2L C2L).
People & Engineering Health
Direct people management for 812 senior engineers and tech leads: 1:1s KRAs performance calibration promotion cases and growth planning.
Hire and ramp senior engineers own the technical bar in interviews and the first-90-days experience.
Champion engineering culture: internal tech talks bootcamps Claude Code adoption and the AI Playbook rollout across the org.
What You Need to Have
8 years building and operating production software with at least 3 years managing engineers (mix of IC and management is fine but you must have managed).
Hands-on technical depth in at least two of: Google Cloud Platform (BigQuery Cloud Run GKE/Kubernetes Vertex AI) Looker/LookML Elasticsearch/OpenSearch modern data engineering (Airflow/Dataflow/dbt) or production AI/ML systems.
Working fluency with container and deployment patterns on GCP Kubernetes (GKE) Cloud Run and serverless and the judgement to push back when a team picks the wrong one.
Track record of shipping client engagements end-to-end you have signed off on production go-lives and owned post-launch outcomes not just sprint outputs.
Strong written communication. You can write a clean status update a defensible architecture rationale and a hard email to a client when scope is creeping.
Comfortable in ambiguity. Consulting work changes weekly you should be energised not destabilised by replanning.
Bonus Points For
Direct experience with agentic AI patterns: tool-use RAG multi-agent orchestration evaluation frameworks or building on Anthropic / Google ADK / OpenRouter.
Background in BI migrations (Tableau MicroStrategy Cognos or Power BI to Looker).
Observability or SIEM delivery experience on the Elastic Stack.
Prior consulting or services-firm experience you understand utilisation T&M vs fixed-bid economics and the texture of client politics.
Why This Role Is Worth Your Time
Real scope. You will lead delivery across multiple Fortune 500 and growth-stage clients in parallel not a single product team.
Senior peers. Our engineers are senior by default; you are managing experts not ramping juniors.
AI is not a side project here. Our internal product portfolio (Conversational BI AIResolveX Agentic QA) ships to real users and you will help shape it.
Direct line to the Director of Engineering and the founders. Decisions move in days not quarters.
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.
