Senior Software Engineer - Python, Data Engineering, AI

About this job

Zenoti offers an all-in-one, cloud-based software solution designed specifically for the beauty and wellness industry, empowering businesses to manage online appointment bookings, point of sale, CRM, employee management, and inventory through a comprehensive mobile platform. The Senior Software Engineer for Python, Data Engineering, and AI will play a vital role in designing, building, and operating batch and incremental data pipelines while leveraging modern technologies like Python, PySpark, and cloud data platforms. In a normal day, this role involves creating data models, building API ingestions, extending data quality frameworks, collaborating with cross-functional product and finance analysts, and mentoring junior engineers to elevate overall engineering practices within the team.

Responsibilities

• Design, build, and operate batch and incremental ETL/ELT pipelines using Python, PySpark, AWS, and Docker.
• Model curated and Iceberg tables while tuning Trino and Athena SQL for performance and cost.
• Build reliable external API data ingestion mechanisms featuring incremental anchors, retries, and idempotent writes.
• Extend data quality frameworks and anomaly detection checks while owning pipeline alerting and on-call duties.
• Lead workstreams on the Databricks-on-Azure lakehouse including Delta Lake tables, workflows, and migration logic.
• Ship code via pull requests using infrastructure-as-code and participate in rigorous design and code reviews.
• Partner with analysts to define metrics and expose clean datasets to QuickSight and AI layers.

Requirements

• 5 to 7 years of hands-on data engineering experience building production-grade data pipelines.
• Strong Python 3.x skills including pandas, pyarrow, and writing testable modules.
• Extensive experience with Apache Spark and PySpark at scale for DataFrame manipulation and shuffle tuning.
• Advanced SQL skills on distributed engines like Trino, Athena, Spark SQL, or Databricks SQL.
• Solid understanding of lakehouse fundamentals, columnar formats, and open table formats like Apache Iceberg or Delta Lake.
• Cloud data platform experience on AWS or Azure covering object storage, serverless compute, and catalogs.
• Proficiency in DAG-based workflow orchestration and managing incremental data ingestion from REST APIs.

Good to have

• Hands-on experience with Databricks or the Azure data stack.
• Familiarity with Infrastructure as Code tools such as Terraform or CloudFormation.
• Experience with streaming technologies like Kafka or Kinesis.
• Exposure to AI and LLM data surfaces, vector stores, and semantic modeling.
• Prior experience mentoring or leading a small pod of engineers.

Who should apply

This role is ideal for experienced data engineering professionals with a solid production background in building scalable data pipelines using Python, PySpark, and modern lakehouse architectures. Candidates who possess strong collaborative skills, enjoy mentoring junior engineers, and have deep familiarity with cloud ecosystems like AWS or Azure will find this position a great fit. Please note that this is a senior position requiring several years of dedicated data engineering experience; freshers are not eligible to apply.

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