Senior Software Engineer - Python, Data Engineering, AI
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LocationHyderabad, India, Hyderabad, Telangana, India
Typefull-time
SenioritySenior
Experience5–7 yrs
DepartmentProduct
Company size1,001–5,000 people
PostedSep 29, 2026 · 1mo ago
Verified live3d ago
At a glanceSummarised by Seekless from the posting.
5-7 years of data engineering in production
Strong Python (3.x): pandas/pyarrow, packaging, virtual envs, writing testable modules
Apache Spark / PySpark at scale: DataFrame API, partitioning, joins/skew, shuffle tuning
Advanced SQL on a distributed engine: window functions, CTEs, incremental/merge patterns, query-plan-level tuning
Lakehouse fundamentals: columnar formats (Parquet), partitioning, experience with Apache Iceberg or Delta Lake
Cloud data platform on AWS or Azure: object storage, serverless/managed compute, catalog, IAM/RBAC
Orchestration of DAG-based workflows with dependency management, retries, failure alerting
Incremental ingestion from REST APIs and databases: pagination, rate limits, watermark-based CDC, idempotent upserts, backfill design
Git + PR-based workflow + CI/CD
Data quality & observability mindset: row-count/freshness/schema checks, alerting, root-causing
Clear written communication: design docs, runbooks, PR descriptions
Databricks hands-on: Delta Live Tables, Unity Catalog, Workflows, Photon, cluster sizing, Databricks Asset Bundles
Azure data stack: ADLS Gen2, Azure Data Factory, Azure Key Vault, Entra ID service principals, Azure DevOps or GitHub Actions
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
Migration experience — moving pipelines/data between clouds or to a managed lakehouse
Streaming/near-real-time: Kafka, Kinesis, Event Hubs, Spark Structured Streaming
Relational sources: SQL Server / MySQL extraction (pyodbc, CDC), reverse-ETL
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, 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
Skills
Python
PySpark
AWS Glue
AWS Lambda
AWS Batch
Docker
Trino
Athena
Apache Iceberg
Delta Lake
Databricks
Terraform
CloudFormation
Git
CI/CD
Apache Spark
Parquet
Azure Data Factory
Azure DevOps
Kafka
Kinesis
QuickSight
Power BI
Benefits
Equal employment opportunity
About the company
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
Responsibilities
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).
Requirements
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.
Nice to have
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.
Legal
*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.