About The Role
CLOUDSUFI, a Google Premium Partner specializing in data and AI solutions, is seeking a Staff / Principal Tech Lead to drive the technical execution of our Google Data Commons program. This is a high-visibility, horizontal leadership role embedded within the Google ecosystem — based physically at Google's Bangalore office — working in close daily collaboration with Google's core Data Commons engineering team. The Tech Lead is the technical spine of the engagement. They sit across all four delivery areas — Data Engineering, Frontend, ML/AI, and DevOps/Infrastructure — providing architectural direction, resolving cross track dependencies, and ensuring the quality and coherence of everything we ship. Equally critical is the ability to represent CloudSufi in a credible, articulate, and collaborative manner to Google counterparts at every level.
This is a genuinely hands-on role: the successful candidate must be able to write, debug, and reason about Python and GCP code themselves — not only direct others or lean on AI coding assistants — and must be comfortable operating in an open-source, public-data environment without relying, for example, on Google internal (google3) tooling.
Technology Stack & Domain Knowledge
Core / Must-Have
- Relevant experience on Knowledge Graph, Statistical Data and Analytics (any experience with Google Data Commons is a nice to have, but not required)
- Google Cloud Spanner – schema design, distributed transactions, interleaved tables, and performance tuning at scale.
- Google BigQuery – data modeling, partitioning/clustering strategies, query optimization, and integration with downstream consumers.
- Data pipelines – Apache Beam / Dataflow, or equivalent GCP-native ETL tooling.
- Infrastructure as Code – Terraform on GCP; Cloud Build, Artifact Registry, GKE or Cloud Run.
- Demonstrable, autonomous hands-on proficiency in Python and native GCP tooling — able to code and debug independently in a live technical discussion, with AI-assisted development as a complement to (not a replacement for) that proficiency.
- Direct experience working with open, public, and unstructured datasets (e.g. sourcing, cleaning, and integrating public statistics or open data feeds) using open-source or standard GCP-native tooling.
- Solid grounding in knowledge graph vs data warehouse principles, and how to design for schema and data drift in an open-source knowledge graph context.
- CI/CD experience and working with GitHub
- Full stack experience, specially with data centric apps/systems Strong Advantage
- Python (primary language for Data Commons import tooling and ML pipelines).
- TypeScript / React for the Data Commons web frontend and visualization layers.
- Vertex AI, BigQuery ML, or equivalent ML lifecycle tooling.
- Knowledge graph principles, RDF/SPARQL, or statistical data modeling.
- DataCommons Python / REST APIs and the DCID import automation tools.
- Experience with Google's internal engineering culture, tools (e.g. Buganizer, Critique, Cider), or prior delivery inside a Google product or partnership engagement.
Experience & Qualifications Required
- 10+ years of software engineering experience, with at least 3 years in a formal or informal tech lead capacity overseeing multiple workstreams.
- Demonstrable experience delivering production-grade systems on Google Cloud Platform.
- Prior experience working with or for Google — as a Googler, through a Google partnership program, or as a contractor embedded in a Google team — is strongly preferred.
- Exceptional verbal and written English communication skills; able to engage confidently with senior Google engineers and program managers.
- Proven ability to operate across ambiguous, fast-moving programs with multiple parallel tracks.
- Based in Bangalore, India, and able to work on-site at Google's Bangalore office on a regular basis.
- Able to read and interpret an RFP / SOW and connect its terms to a workable technical delivery plan.
Skills:- Google Cloud Platform (GCP), Python and Data engineering
