{"id":1472824,"url":"https://alion.io/job/zenoti-senior-software-engineer-python-data-engineering-ai","title":"Senior Software Engineer - Python, Data Engineering, AI","company":{"id":58903,"name":"Zenoti","domain":"zenoti.com","url":"https://alion.io/company/zenoti","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"A","score":95,"open_postings":40,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":208,"computed_at":"2026-09-29T05:45:00Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Hyderabad, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":16500,"max_usd":34000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":51},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Airflow","optional":false},{"name":"Amazon Redshift","optional":false},{"name":"Amazon S3","optional":false},{"name":"Apache Iceberg","optional":false},{"name":"AWS","optional":false},{"name":"AWS Lambda","optional":false},{"name":"AWS Step Functions","optional":false},{"name":"Azure","optional":false},{"name":"BigQuery","optional":false},{"name":"CI/CD","optional":false},{"name":"CloudFormation","optional":false},{"name":"Dagster","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"Docker","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Git","optional":false},{"name":"Google BigQuery","optional":false},{"name":"IAM","optional":false},{"name":"Intercom","optional":false},{"name":"Jira","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"New Relic","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Rest API","optional":false},{"name":"Shuffle","optional":false},{"name":"Snowflake","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false},{"name":"Trino","optional":false},{"name":"Amazon Kinesis","optional":true},{"name":"Apache Kafka","optional":true},{"name":"Azure Data Factory","optional":true},{"name":"Azure DevOps","optional":true},{"name":"Bicep","optional":true},{"name":"dbt","optional":true},{"name":"GitHub Actions","optional":true},{"name":"LLM","optional":true},{"name":"Microsoft Entra ID","optional":true},{"name":"MySQL","optional":true},{"name":"Pinecone","optional":true},{"name":"Power BI","optional":true},{"name":"Tableau","optional":true}],"status":"live","first_seen_at":"2026-09-29T05:40:24Z","employer_posted_date":"2026-09-29","last_verified_at":"2026-09-30T01:41:35Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"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\nZenoti 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.\nOur 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\nWhat you'll do\nDesign, build, and operate batch and incremental ETL/ELT pipelines in Python (Glue Python-shell, PySpark, AWS Batch/Docker, Lambda).\nModel curated and Iceberg tables; write and tune Trino/Athena SQL; own partitioning, compaction, and cost/performance of the lake.\nBuild ingestion from external APIs (Salesforce, Adyen, Intercom, Jira, New Relic, etc.) with incremental anchors, retries, and idempotent writes.\nExtend the data-quality framework and anomaly-detection checks; own alerting and on-call for pipeline health.\nLead workstreams on the Databricks-on-Azure lakehouse: Delta Lake tables, Databricks Workflows/Jobs, Unity Catalog, and migration of existing curation logic.\nShip via PRs with tests, infra-as-code (CloudFormation / Terraform), and CI/CD; participate in code review and design reviews.\nPartner with Product, Finance, and Customer Success analysts on metric definitions; expose datasets to QuickSight and to the AI/MCP layer with clear, documented semantics.\nMentor junior engineers and raise the bar on engineering practices (testing, observability, documentation).\nMust have\n5-7 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.\nStrong Python (3.x): pandas/pyarrow, packaging, virtual envs, writing testable modules and shared libraries - not just notebooks.\nApache Spark / PySpark at scale: DataFrame API, partitioning, joins/skew, shuffle tuning, reading/writing Parquet.\nAdvanced 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.\nLakehouse 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).\nCloud 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.\nOrchestration of DAG-based workflows with dependency management, retries, and failure alerting (Step Functions, Airflow, Databricks Workflows, Dagster, or equivalent).\nIncremental ingestion from REST APIs and databases: pagination, rate limits, watermark/anchor-based CDC, idempotent upserts, backfill design.\nGit + 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.\nData quality & observability mindset: row-count/freshness/schema checks, alerting on failures, and root-causing a bad number in a dashboard back to its source.\nClear written communication: design docs, runbooks, PR descriptions that a reviewer can follow.\nGood to have\nDatabricks 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.\nAzure data stack: ADLS Gen2, Azure Data Factory, Azure Key Vault, Entra ID service principals, Azure DevOps or GitHub Actions for deployment.\nAWS depth: Glue (Python-shell and Spark), Athena v3/Trino internals, Iceberg on Athena, Step Functions, Batch, ECR, CloudFormation, CodeBuild/CodePipeline, Secrets Manager.\nInfrastructure as code: CloudFormation, Terraform, or Bicep.\nDocker for packaging batch jobs; basic familiarity with .NET-based jobs coexisting in a Python pipeline.\nMigration experience - moving pipelines/data between clouds or from a hand-rolled lake to a managed lakehouse, including parity validation.\nStreaming/near-real-time: Kafka, Kinesis, Event Hubs, Spark Structured Streaming.\nRelational sources: SQL Server / MySQL extraction (pyodbc, CDC), reverse-ETL back into an application DB.\nBI serving: QuickSight, Power BI, or Tableau - dataset design, row-level security, SPICE/import vs direct query trade-offs.\nAI/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.\nStatistics for data quality: anomaly detection, churn/adoption scoring, or similar analytical pipelines.\nSaaS-domain familiarity: subscription billing (Zuora), payments (Adyen), CRM (Salesforce), or support/telephony data (Intercom, Gong, RingCentral).\nExperience mentoring or leading a small pod of engineers.\nZenoti 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.","description_format":"text","description_chars":6583,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"phd","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Artificial Intelligence","POS Systems","Scheduling & Booking Software"],"lifecycle":[{"event":"open","at":"2026-09-29T17:14:05Z"}],"liveness":{"score":86,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.86,"p_room":1,"age_days":0,"expected_fill_days":208,"reasons":["conf:3","win:early"],"computed_at":"2026-09-30T05:18:40Z"},"pay":null,"html_url":"https://alion.io/job/zenoti-senior-software-engineer-python-data-engineering-ai","json_url":"https://alion.io/job/zenoti-senior-software-engineer-python-data-engineering-ai.json","meta":{"generated_at":"2026-09-30T05:18:40Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":3688,"day_limit":5000,"remaining_today":1312,"minute_limit":60,"resets_at":"2026-10-01T00:00:00Z"}}}