Senior Software Engineer - Python, Data Engineering, AI

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. •…