Senior Software Engineer - Python, Data Engineering, AI
About this role
Engineers who thrive on building robust data platforms and mentoring others will find a great home in this position at Zenoti. The team develops cloud-based software solutions for the beauty and wellness industry, powering thousands of clients globally through scalable engineering.
In this role, the candidate will design and operate ETL pipelines, model lakehouse tables, and integrate external APIs while ensuring high data quality and reliability. The position exists to scale the data infrastructure supporting product, finance, and AI initiatives, making it ideal for a meticulous engineer with a strong background in distributed data processing and cloud architecture.
What you will do
- Design, build, and operate batch and incremental ETL/ELT pipelines using Python and associated AWS or Azure tools.
- Model curated and Iceberg tables while writing and tuning Trino and Athena SQL for optimal performance.
- Build resilient ingestion mechanisms from external APIs with support for incremental anchors, retries, and idempotent writes.
- Extend the existing data-quality framework and anomaly-detection checks to maintain high standards of data health.
- Lead workstreams on the Databricks-on-Azure lakehouse including Delta Lake tables and migration of curation logic.
- Partner with cross-functional analysts to define metrics and expose governed datasets to QuickSight and AI layers.
- Mentor junior engineers and elevate team engineering practices around testing, observability, and documentation.
What you need
- Five to seven years of professional data engineering experience building and operating production pipelines.
- Strong Python 3.x skills including pandas, pyarrow, packaging, and writing testable modules.
- Extensive experience with Apache Spark and PySpark at scale for DataFrame operations and performance tuning.
- Advanced SQL capabilities on distributed engines utilizing window functions, CTEs, and query-plan optimization.
- Hands-on lakehouse fundamentals with columnar formats and open table formats such as Apache Iceberg or Delta Lake.
- Cloud data platform experience on AWS or Azure covering object storage, compute, catalogs, and access control.
- Familiarity with DAG-based workflow orchestration, incremental API ingestion, and Git-based CI/CD workflows.
Nice to have
- Databricks hands-on experience and associated certifications
- Azure data stack and AWS depth proficiency
- Infrastructure as code using Terraform, CloudFormation, or Bicep
- Streaming or near-real-time data processing experience
- AI and LLM data surface exposure including vector stores and semantic modeling
Skills and tech stack
Questions candidates ask
What tools and technologies will I be using daily?
Daily work involves Python, PySpark, AWS, Azure, Trino, Athena, Apache Iceberg, Databricks, and infrastructure-as-code tools like Terraform or CloudFormation. You will also interact with orchestration tools and Git-based CI/CD pipelines.
What kind of experience is required for this role?
Candidates must have five to seven years of data engineering experience in production, building and operating pipelines that other teams depend on daily. Pure analyst, BI-developer, or drag-and-drop ETL tool experience does not count toward this requirement.
What will I actually be working on?
You will design batch and incremental ETL pipelines, model lakehouse tables, tune distributed SQL queries, build API ingestion mechanisms, and extend data quality frameworks. You will also lead workstreams on the Databricks-on-Azure lakehouse and mentor junior engineers.