{"id":2199570,"url":"https://alion.io/job/cit-tech-lead-data-databricks","title":"Tech Lead Data - Databricks","company":{"id":957,"name":"CI&T","domain":"ciandt.com","url":"https://alion.io/company/ciandt","size_band":"5000+","is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"A","score":94,"open_postings":21,"ghost_share":0,"stale_share":0.429,"repost_share":0,"time_to_fill_p50_days":15,"computed_at":"2026-10-15T05:46:00Z"}},"role":"Leadership","role_family":"Leadership","seniority":"lead","employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Brazil"],"countries":["BR"],"hiring_countries":["BR"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":48000,"max_usd":138000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":2046},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Active Directory","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"Git","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Apache Kafka","optional":true},{"name":"dbt","optional":true},{"name":"Great Expectations","optional":true}],"status":"live","first_seen_at":"2026-10-09T19:39:19Z","employer_posted_date":"2026-10-09","last_verified_at":"2026-10-15T15:47:47Z","board_verified":true,"closed_at":null,"days_open":5,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":5},"description":"About the Role\nWe are looking for a senior, hands-on Data Tech Lead to drive the technical leadership of our data engineering initiatives on Databricks in Azure.\nYou will partner directly with the Data Architect to define standards and references, remove roadblocks, and enable the Data Engineering team to deliver scalable, reliable pipelines aligned with the business within the Medallion architecture.\nKey Responsibilities\nProvide technical leadership to Data Engineering squads, supporting design, reviews, and decision-making to unblock delivery.\n\nCo-define with the Data Architect the technical vision, coding standards, modeling conventions, and best practices for the Lakehouse (Delta Lake + Medallion).\n\nDesign and oversee pipelines in Databricks (PySpark/Spark SQL) across Bronze/Silver/Gold layers, ensuring security, performance, and governance.\n\nImplement and reinforce DataOps practices: version control (Git), CI/CD, testing (unit/integration/data quality), and documentation.\n\nOptimize jobs/pipelines: partitioning, Spark tuning, cost management, and reliability (baseline observability and alerting).\n\nEnsure non-functional requirements: security, access control, and data policies in Azure/Databricks aligned with defined governance.\n\nCollaborate with stakeholders (business/analytics) to translate requirements into scalable solutions with realistic delivery plans.\n\nMentor engineers, promote standards, and create references (templates, playbooks) to accelerate the team.\n\nRequired Skills and Qualifications\nMust-have Skills\nStrong experience in Data Engineering and projects, with experience as a Tech Lead/Senior leading teams technically and operationally.\n\nStrong hands-on experience in Databricks: PySpark and Spark SQL; batch pipelines (and working knowledge of streaming when needed).\n\nMastery of Delta Lake and practical application of the Medallion architecture (Bronze/Silver/Gold).\n\nSolid experience with Azure for data, including at least: Azure Databricks, Azure Data Lake Storage (ADLS), and integration with security/identity services (e.g., Azure Active Directory).\n\nStrong SQL and Python skills; fundamentals in modeling (dimensional/consumption) and good practices for contracts/schemas.\n\nExperience with DataOps: Git, CI/CD for data pipelines, automated testing, and clear documentation.\n\nProven ability to review design/code, resolve performance/scale issues, and guide teams.\n\nAdvanced English to work with stakeholders and produce technical documentation.\n\nNice-to-have Skills\nUnity Catalog (governance/lineage/access control) and Databricks Workflows/Repos; Delta Live Tables.\n\nData quality frameworks (Great Expectations/Soda) and/or dbt on the Lakehouse.\n\nOrchestration (Airflow/Databricks Workflows) and observability (metrics, logs, alerts).\n\nExperience with CDC/streaming ingestion (Event Hubs/Kafka) when applicable.\n\nConsulting experience and international client exposure; 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