{"id":3026598,"url":"https://alion.io/job/symphoni-hr-databricks-tech-lead","title":"Databricks Tech Lead","company":{"id":3800108,"name":"Symphoni HR","domain":"symphonihr.com","url":"https://alion.io/company/symphoni-hr","size_band":"201-500","is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":"Schema","truth_index":{"grade":"A","score":100,"open_postings":34,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":45,"computed_at":"2026-10-22T06:10:10Z"}},"role":"Leadership","role_family":"Leadership","seniority":"lead","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":30000,"max_usd":78000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":33},"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon Kinesis","optional":false},{"name":"Amazon S3","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Azure DevOps","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"Feature Store","optional":false},{"name":"GCP","optional":false},{"name":"GitHub Actions","optional":false},{"name":"GitLab CI","optional":false},{"name":"Google BigQuery","optional":false},{"name":"IAM","optional":false},{"name":"MLFlow","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false},{"name":"Apache Hudi","optional":true},{"name":"Apache Iceberg","optional":true},{"name":"dbt","optional":true},{"name":"Looker","optional":true},{"name":"Machine Learning","optional":true},{"name":"Power BI","optional":true},{"name":"Tableau","optional":true}],"status":"live","first_seen_at":"2026-10-21T19:13:51Z","employer_posted_date":"2026-10-21","last_verified_at":"2026-10-22T13:50:45Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"The Databricks Tech Lead will design, build, and optimize enterprise-scale data lakehouse solutions on the Databricks platform. You will lead the delivery of financial crime platforms covering AML, KYC, customer risk assessment, sanctions screening, transaction monitoring, fraud detection, and regulatory reporting. This is a hands-on technical leadership role with ownership of architecture, governance, and delivery quality.\nOur client is a leading global technology and analytics company operating in the computer software industry at significant scale. They serve large enterprises with data-driven solutions and are known for a collaborative, engineering-led culture that values innovation and continuous learning.\nResponsibilities\nDesign, build, and maintain Databricks workspaces, clusters, and jobs across dev, test, and prod environments, including Unity Catalog governance and access control.\nDesign Delta Lake tables and Medallion (Bronze/Silver/Gold) architectures, and implement Delta Live Tables pipelines with data quality expectations.\nDevelop scalable batch and streaming pipelines using PySpark, Spark SQL, and Structured Streaming from Kafka, Event Hubs, or Kinesis.\nSupport MLflow, Feature Store, and model serving workloads, including GenAI and RAG use cases.\nBuild CI/CD pipelines and infrastructure-as-code using Databricks Asset Bundles or Terraform, integrated with Azure DevOps, GitHub Actions, or GitLab CI.\nImplement row-level security, column masking, performance tuning, and cost monitoring.\nDocument architecture, design decisions, and operational runbooks.\nLead and mentor engineers, driving code quality, best practices, and delivery standards.\nRequirements\n8+ years of total data or software engineering experience, with 5+ years of hands-on Databricks experience in production (3+ years minimum).\nDeep expertise in Databricks Workspaces, Jobs, Workflows, Unity Catalog, Delta Lake internals, and Delta Live Tables.\n4+ years of PySpark development and expert-level SQL, including Spark performance tuning (AQE, partitioning, caching, query plans).\nProficiency in Python for pipeline development and automation.\nHands-on experience with at least one cloud platform (Azure ADLS Gen2/ADF/Azure Databricks, AWS S3/EMR/Glue, or GCP GCS/BigQuery/Dataproc), including networking, IAM, and service principal authentication.\nBachelor's or Master's degree in Computer Science, Information Technology, Data Engineering, or a related field.\nNice to have: working knowledge of MLflow, dbt, Kafka/Confluent, Terraform, Apache Iceberg or Hudi, and BI tools such as Power BI, Tableau, or Looker.\nNice to have: Databricks certifications (Data Engineer Associate/Professional, Machine Learning, or Apache Spark Developer) or cloud data engineering certifications.\nSkills\nDatabricks, Unity Catalog, Delta Lake, Delta Live Tables, PySpark, Spark SQL, Python, Structured Streaming, Cloud platform (Azure, AWS, or GCP), MLflow, Feature Store, dbt, Kafka/Confluent, Terraform, Apache Iceberg, Apache Hudi, Power BI, Tableau, Looker","description_format":"text","description_chars":3043,"description_truncated":false,"requirements":{"experience_years_min":8,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":["Continuous 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