---
title: 'Senior Software Engineer - Python, Data Engineering, AI at Zenoti'
canonical: 'https://feeny.ai/job/senior-software-engineer-python-data-engineering-ai-zenoti-hyderabad-jhyx2f1y5d3b'
type: 'job'
last_seen: '2026-11-05'
---

# Senior Software Engineer - Python, Data Engineering, AI at Zenoti

- **Company:** Zenoti
- **Location:** Hyderabad, India
- **Posted:** 2026-09-29
- **Last confirmed live:** 2026-11-05
- **Apply:** https://job-boards.greenhouse.io/zenoti/jobs/8004516003

## Job description

Zenoti provides an all-in-one, cloud-based software solution for the beauty and wellness industry. Our solution allows users to seamlessly manage every aspect of the business in a comprehensive mobile solution: online appointment bookings, POS, CRM, employee management, inventory management, built-in marketing programs and more. Zenoti helps clients streamline their systems and reduce costs, while simultaneously improving customer retention and spending. Our platform is engineered for reliability and scale and harnesses the power of enterprise-level technology for businesses of all sizes

Zenoti powers more than 30,000 salons, spas, medspas and fitness studios in over 50 countries. This includes a vast portfolio of global brands, such as European Wax Center, Hand & Stone, Massage Heights, Rush Hair & Beauty, Sono Bello, Profile by Sanford, Hair Cuttery, CorePower Yoga and TONI&GUY.

Our recent accomplishments include surpassing a $1 billion unicorn valuation, being named Next Tech Titan by GeekWire, raising an $80 million investment from TPG, ranking as the 316th fastest-growing company in North America on Deloitte’s 2020 Technology Fast 500™. We are also proud to be recognized as a Great Place to Work CertifiedTM for 2021-2022 as this reaffirms our commitment to empowering people to feel good and find their greatness. To learn more about Zenoti visit: https://www.zenoti.com

## What you'll do

- Design, build, and operate batch and incremental ETL/ELT pipelines in Python (Glue Python-shell, PySpark, AWS Batch/Docker, Lambda).
- Model curated and Iceberg tables; write and tune Trino/Athena SQL; own partitioning, compaction, and cost/performance of the lake.
- Build ingestion from external APIs (Salesforce, Adyen, Intercom, Jira, New Relic, etc.) with incremental anchors, retries, and idempotent writes.
- Extend the data-quality framework and anomaly-detection checks; own alerting and on-call for pipeline health.
- Lead workstreams on the Databricks-on-Azure lakehouse: Delta Lake tables, Databricks Workflows/Jobs, Unity Catalog, and migration of existing curation logic.
- Ship via PRs with tests, infra-as-code (CloudFormation / Terraform), and CI/CD; participate in code review and design reviews.
- Partner with Product, Finance, and Customer Success analysts on metric definitions; expose datasets to QuickSight and to the AI/MCP layer with clear, documented semantics.
- Mentor junior engineers and raise the bar on engineering practices (testing, observability, documentation).

Must have

- 5-7 years of data engineering in production: building, deploying, and operating pipelines that other teams depend on daily. Analyst, BI-developer, or drag-and-drop ETL-tool-only experience does not count toward this.
- Strong Python (3.x): pandas/pyarrow, packaging, virtual envs, writing testable modules and shared libraries — not just notebooks.
- Apache Spark / PySpark at scale: DataFrame API, partitioning, joins/skew, shuffle tuning, reading/writing Parquet.
- Advanced SQL on a distributed engine (Trino/Athena, Spark SQL, Databricks SQL, BigQuery, Snowflake, or Redshift): window functions, CTEs, incremental/merge patterns, query-plan-level tuning.
- Lakehouse fundamentals: columnar formats (Parquet), partitioning strategies, and hands-on experience with at least one open table format — Apache Iceberg or Delta Lake (schema evolution, time travel, compaction/OPTIMIZE, MERGE INTO).
- Cloud data platform on AWS or Azure — at minimum object storage (S3/ADLS), serverless or managed compute (Glue/EMR/Lambda or ADF/Synapse/Functions), a catalog (Glue Data Catalog / Unity Catalog / Hive), and IAM/RBAC basics.
- Orchestration of DAG-based workflows with dependency management, retries, and failure alerting (Step Functions, Airflow, Databricks Workflows, Dagster, or equivalent).
- Incremental ingestion from REST APIs and databases: pagination, rate limits, watermark/anchor-based CDC, idempotent upserts, backfill design.
- Git + PR-based workflow + CI/CD — you've shipped through a review gate and a promotion path (dev → qa → prod) and can debug a failing build.
- Data quality & observability mindset: row-count/freshness/schema checks, alerting on failures, and root-causing a bad number in a dashboard back to its source.
- Clear written communication: design docs, runbooks, PR descriptions that a reviewer can follow.

Good to have

- Databricks hands-on (any cloud): Delta Live Tables / Lakeflow, Unity Catalog, Workflows, Photon, cluster/SQL-warehouse sizing, Databricks Asset Bundles. Databricks Data Engineer Associate/Professional certification is a plus.
- Azure data stack: ADLS Gen2, Azure Data Factory, Azure Key Vault, Entra ID service principals, Azure DevOps or GitHub Actions for deployment.
- AWS depth: Glue (Python-shell and Spark), Athena v3/Trino internals, Iceberg on Athena, Step Functions, Batch, ECR, CloudFormation, CodeBuild/CodePipeline, Secrets Manager.
- Infrastructure as code: CloudFormation, Terraform, or Bicep.
- Docker for packaging batch jobs; basic familiarity with .NET-based jobs coexisting in a Python pipeline.
- Migration experience — moving pipelines/data between clouds or from a hand-rolled lake to a managed lakehouse, including parity validation.
- Streaming/near-real-time: Kafka, Kinesis, Event Hubs, Spark Structured Streaming.
- Relational sources: SQL Server / MySQL extraction (pyodbc, CDC), reverse-ETL back into an application DB.
- BI serving: QuickSight, Power BI, or Tableau — dataset design, row-level security, SPICE/import vs direct query trade-offs.
- AI/LLM data surfaces: exposing governed datasets to agents via MCP or similar, vector stores (Pinecone), metadata/catalog curation for LLM consumption, dbt-style semantic modeling.
- Statistics for data quality: anomaly detection, churn/adoption scoring, or similar analytical pipelines.
- SaaS-domain familiarity: subscription billing (Zuora), payments (Adyen), CRM (Salesforce), or support/telephony data (Intercom, Gong, RingCentral).
- Experience mentoring or leading a small pod of engineers.

Zenoti provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training.

## About Zenoti

## Company Overview
- **One-liner**: Zenoti provides an all-in-one, AI-powered cloud platform for managing operations, marketing, and customer experience in the beauty, wellness, and fitness industries.
- **Entity Type**: Private (funded; Series not specified, but last known valuation $1.5B in 2021)
- **Headquarters**: Bellevue, Washington, USA (Seattle area) with major offices in Hyderabad, India
- **Founded**: 2010
- **Founders**: Sudheer Koneru and Dheeraj Koneru

## Core Business
- **Primary industries**: Beauty and wellness (salons, spas, medspas), fitness centers, and now expanding into gyms.
- **Target customers**: B2B – small-to-large chains, single-location businesses, enterprise multi-location brands (30,000+ businesses across 50+ countries).
- **Mission / purpose**: “Help our Spa, Salon, Medspa and Fitness owners feel good – so their customers feel GREAT.”

## Products & Services
- **Zenoti OS**: The core cloud-based platform integrating booking, POS, CRM, payroll, inventory, marketing, online booking, and sales. Designed for reliability and scale with a 99.9% uptime SLA.
- **AI Workforce**: A suite of AI agents deployed to automate front-office and back-office tasks: AI Receptionist (handles phone calls), Concierge, Lead Manager, Marketer, Scribe, and Dispute Manager.
- **Zeenie**: An AI assistant that automates tasks, generates reports, finds information, and creates marketing content.
- **Predictive analytics / Core ML models**: Slot prediction, demand forecasting, and customer insight tools built on aggregated data from 30,000+ businesses.

## Market Standing
- **Valuation**: $1.5 billion (2021) – [GeekWire article](#)
- **Key Metric**: Annual revenue grew nearly 40% in 2024; company is profitable – [GeekWire article](#)
- **Notable Investors / Partners**: Advent International, TPG, Tiger Global, Steadview Partners, Norwest Venture Partners, Accel Partners (total funding $330M) – [GeekWire article](#)
- **Growth Signals**: Expansion into fitness/gym sector; new executive hires (CFO Sean Blitchok, CCO Steve Hudek, SVP Marketing Doug Sechrist, President/COO Neal Singh); Deloitte Fast 500 revenue growth recognition in 2024 – [GeekWire article](#), [About page](#)

## Competitive Advantages
- **Comprehensive data + AI integration**: Zenoti OS tightly couples end-to-end workflows with rich cross-customer data, allowing AI agents to be highly effective – a moat competitors lack.
- **Industry-specific, cloud-native**: Built from the ground up for beauty/wellness, not a generic ERP; proven with 30,000+ businesses and 50+ countries.
- **High adoption and reliability**: 98% staff adoption within first week of training; 99.9% uptime SLA.
- **Profitable growth**: Unlike many SaaS peers, Zenoti is profitable while still growing rapidly, indicating strong unit economics.

## Strategic Focus
- **AI automation as a growth lever**: Deploying conversational AI (receptionist, lead management) and predictive analytics to help customers increase revenue and efficiency.
- **Adjacent market expansion**: Targeting fitness centers and gyms with a similar enterprise-grade solution.
- **Potential IPO readiness**: CEO stated the company is “capable of going public” but is currently focused on growth and M&A opportunities – [GeekWire article](#)
- **Continued platform investment**: Hiring heavily in product management (Payments, AI Workflows, APIs) and customer success – [Greenhouse job board](#)

## Why Work Here
- **Culture**: Values include transparency, bold action, continuous learning, and “Hold Nothing Back.” Emphasis on diversity, equity, and inclusion with employee resource groups and community partnerships – [Careers page](#).
- **Growth opportunities**: Structured learning, internal mobility, and scaling fast – “your career will too.” Recognition programs and global team collaboration.
- **Work environment**: Cross-functional teams across time zones; offices in Hyderabad, Bellevue, and other global locations. Flexible and remote-friendly work style (implied by distributed teams).
- **Impact**: Work on a platform that powers the world’s best beauty and wellness brands, with a direct impact on customers’ bottom lines. The Zenoti Foundation supports education, wellness, and sustainability.
- **Recognition**: Gold and Silver cybersecurity innovation awards; G2 High Performer ratings – [About page](#)

## Sources
1. [Zenoti.com – AI Workforce and platform overview](https://www.zenoti.com/)
2. [Zenoti Careers page – Culture, DEI, values](https://www.zenoti.com/company/careers)
3. [Greenhouse job board – Open roles and locations](http://job-boards.greenhouse.io/zenoti)
4. [Zenoti About page – Story, awards, global footprint](https://zenoti.com/company/about-us)
5. [GeekWire – AI expansion, funding, revenue, fitness push](https://www.geekwire.com/2025/zenoti-bets-big-on-ai-as-profitable-software-maker-expands-beyond-beauty-sector-and-into-gyms/)

## Other roles at Zenoti

- [Director HR Partnership and Talent Acquisition](https://feeny.ai/job/director-hr-partnership-and-talent-acquisition-zenoti-seattle-252pht86n07e) — Seattle, WA
- [Vice President, Product Marketing](https://feeny.ai/job/vice-president-product-marketing-zenoti-seattle-fwt3m0s2ha6e) — Seattle, WA
- [Senior Network Reliability Engineer](https://feeny.ai/job/senior-network-reliability-engineer-zenoti-hyderabad-9ab708t9k99m) — Hyderabad, India
- [Senior Manager -  Product Management (Payments/Fintech)](https://feeny.ai/job/senior-manager-product-management-payments-fintech-zenoti-hyderabad-1je8trqhmn7w) — Hyderabad, India
- [Senior Implementation Consultant](https://feeny.ai/job/senior-implementation-consultant-zenoti-hyderabad-694zhakp7ztg) — Hyderabad, India
- [Senior Implementation Consultant](https://feeny.ai/job/senior-implementation-consultant-zenoti-hyderabad-ybz0dxk10kkc) — Hyderabad, India
- [Lead Product Manager(SaaS + AI)](https://feeny.ai/job/lead-product-manager-saas-ai-zenoti-hyderabad-sjed7ngakh4c) — Hyderabad, India
- [Lead Reliability Engineer - Network and Security](https://feeny.ai/job/lead-reliability-engineer-network-and-security-zenoti-hyderabad-xc49etngrg15) — Hyderabad, India
- [Lead Site Reliability Engineer  -   Platform Engineering / SRE](https://feeny.ai/job/lead-site-reliability-engineer-platform-engineering-sre-zenoti-hyderabad-qvtnngjzcgrf) — Hyderabad, India
- [Product Support Consultant - (2 to 5 yrs exp) - L1/L2 Client Support(Calls) - Night Shift](https://feeny.ai/job/product-support-consultant-2-to-5-yrs-exp-l1-l2-client-support-calls-night-kzmzmxydha4f) — Hyderabad, India