{"id":3399466,"url":"https://alion.io/job/hcltech-azure-data-architect-2","title":"Azure Data Architect","company":{"id":225,"name":"HCLTech","domain":"hcltech.com","url":"https://alion.io/company/hcltech","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SuccessFactors","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"staff","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":28000,"max_usd":53000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":17},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Anomaly Detection","optional":false},{"name":"Apache Kafka","optional":false},{"name":"Azure","optional":false},{"name":"Azure Data Factory","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"Dimensional Modeling","optional":false},{"name":"ETL/ELT","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Great Expectations","optional":false},{"name":"Jenkins","optional":false},{"name":"MLFlow","optional":false},{"name":"pySpark","optional":false},{"name":"Pytest","optional":false},{"name":"Python","optional":false},{"name":"Rest API","optional":false},{"name":"Scala","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-10-28T13:45:58Z","employer_posted_date":"2026-10-28","last_verified_at":"2026-10-28T18:57:08Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"Job Summary\nJob Title: Azure Databricks Architect\nJob Summary:\nThe Azure Databricks Architect is responsible for designing, building, and optimizing scalable data platforms and analytics solutions on Microsoft Azure using Azure Databricks. The role involves architecting end-to-end data pipelines, ensuring data governance, and enabling advanced analytics and AI solutions.\nKey Responsibilities\nKey Responsibilities:\nDesign and implement scalable data architectures using Azure services\nBuild and optimize ETL/ELT pipelines using Azure Databricks and Apache Spark\nArchitect data lake and data warehouse solutions using Azure Data Lake and Azure Synapse Analytics\nDefine data modeling strategies (batch & real-time processing)\nEnsure data security, governance, and compliance standards\nCollaborate with stakeholders to understand business requirements and translate them into technical solutions\nOptimize performance and cost efficiency of data workloads\nImplement CI/CD pipelines for data engineering workflows\nMentor and guide data engineers and development teams\nIntegrate advanced analytics, AI, and ML solutions when required\nSkill Requirements\nRequired Skills & Qualifications:\nStrong experience with Azure Databricks and Spark (PySpark/Scala)\nHands-on experience with Azure services (ADF, ADLS, Synapse, Event Hub)\nExpertise in big data architecture and distributed systems\nStrong knowledge of SQL, Python, and data engineering concepts\nExperience with data modeling techniques (star schema, dimensional modeling)\nUnderstanding of real-time streaming (Kafka/Event Hub)\nKnowledge of DevOps and CI/CD practices\nStrong Development Area:\nData Engineering Foundations\nBatch vs streaming; lakehouse concepts; medallion (Bronze/Silver/Gold); file formats (Parquet/Delta/CSV/JSON/Avro); partitioning & clustering; schema evolution; data governance basics; DevOps/CI-CD for data.\nSQL\nJoins, subqueries, CTEs, window functions, set operations; aggregation & rollups; MERGE/UPSERT; analytic functions; performance (indexes, partition pruning, statistics); data validation scenarios (dedupe, top-N, SCD keys).\nApache Spark (Core)\nSpark architecture (driver/executors), DAG, stages/tasks; RDD vs DataFrame/Dataset; wide vs narrow transformations; shuffle mechanics; caching/persistence; partitioning; broadcast joins; skew handling; checkpointing; job tuning.\nPySpark\nDataFrame API, Spark SQL; UDF vs pandas UDF; windowing; incremental loads; structured streaming (triggers, watermarks); handling semi-structured data; optimizing with predicates, pushdown, join strategies; error handling; unit testing (pytest + chispa).\nDatabricks Platform\nWorkspace basics; clusters (Single Node/All-Purpose/Jobs), cluster policies; DBR/LTS; notebooks & Repos; Jobs & Workflows; Delta Lake & Delta Live Tables; Unity Catalog (catalog/schema/table, permissions, lineage); MLflow basics; secret scopes; DBFS; REST APIs; Databricks Connect.\nDelta Lake / Lakehouse Patterns\nACID transactions; schema enforcement/evolution; time travel; OPTIMIZE/ZORDER; VACUUM; CDC patterns (MERGE INTO, change data feed); streaming vs batch Delta; expectations/constraints; table maintenance strategies.\nOrchestration & Scheduling\nDatabricks Workflows; Azure Data Factory/Synapse pipelines; triggers; parameter passing; fail/retry; alerts; integration with AutoSys/Control-M/Jenkins/GitHub Actions; event-driven patterns.\nPython (Core for Data)\nCore syntax; typing & data structures; file I/O; logging; virtual environments; packaging; testing (pytest); common data libs (pandas, pyarrow); error handling; performance considerations (vectorization, generators).\nData Quality & Testing\nGreat Expectations/dbx expectations or custom checks; unit/integration tests; reconciliation (row/amount); anomaly detection; contract testing for schemas; data observability (metrics, SLAs, freshness).\nSecurity & Governance\nUnity Catalog permissions, row/column-level security; secrets management (Key Vault/Secret scopes); PII handling; audit logs; token management; compliance basics.\nCost & Performance Optimization\nCluster sizing, autoscaling; DBU awareness; spot/preemptible instances; storage formats; caching; efficient joins; job scheduling; monitoring with metrics & Ganglia/Spark UI; cost tagging and chargeback.\nAnalytical Skills & Problem Solving\nBreak down data problems; root-cause incidents; propose alternatives; estimate complexity; communicate clearly with stakeholders.\nStake Holder Management\nInteraction with Client Stakeholders, Communication.\nOther Requirements\n1. Microsoft Certified: Azure Solutions Architect Expert (Recommended)\n2. 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