{"id":1999023,"url":"https://alion.io/job/hcltech-technical-specialist-41","title":"Technical Specialist","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":"Sales","role_family":"Sales","seniority":null,"employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":["Hyderabad, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Apache Kafka","optional":false},{"name":"BigQuery","optional":false},{"name":"CI/CD","optional":false},{"name":"Dimensional 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efficiency.\nIntegrate structured and unstructured data from applications, APIs, databases, files, and event streams.\nPrepare curated, reusable datasets and feature pipelines for model training, validation, deployment, and monitoring.\nImplement data-quality checks, schema validation, lineage, metadata, retention, and access controls.\nDevelop reliable orchestration, CI/CD, automated testing, observability, and incident-response practices for data workloads.\nTroubleshoot pipeline failures, data delays, duplication, schema changes, and performance bottlenecks.\nCollaborate with data scientists to operationalize machine learning workflows and ensure reproducible data inputs.\nDocument data models, interfaces, operational procedures, and architectural decisions.\nMandatory Technical Skills\nStrong programming skills in Python and advanced SQL.\nHands-on experience with Apache Kafka or a managed Kafka service in production environments.\nStrong expertise in Google BigQuery, including data modeling, query optimization, partitioning, clustering, and cost management.\nPractical knowledge of ETL/ELT patterns, dimensional modeling, data lakes, data warehouses, and distributed processing.\nExperience building data pipelines for AI and machine learning workloads.\nKnowledge of machine learning lifecycle concepts, feature engineering, model inputs and outputs, and production monitoring.\nExperience with Google Cloud data services and cloud-native security practices.\nProficiency with workflow orchestration, version control, automated testing, and CI/CD.\nUnderstanding of data governance, privacy, access management, encryption, and audit requirements.\nStrong diagnostic and performance-tuning skills across streaming and analytical workloads\nKey Responsibilities\nAI, Machine Learning, Kafka, Big Query\nDesign and develop batch and real-time data pipelines for analytics, AI, and machine learning use cases.\nBuild Kafka producers, consumers, topics, connectors, and stream-processing workflows with appropriate partitioning, ordering, replay, retry, and dead-letter handling.\nCreate and optimize BigQuery datasets, tables, views, materialized views, partitioning, clustering, and SQL workloads for performance and cost efficiency.\nIntegrate structured and unstructured data from applications, APIs, databases, files, and event streams.\nPrepare curated, reusable datasets and feature pipelines for model training, validation, deployment, and monitoring.\nImplement data-quality checks, schema validation, lineage, metadata, retention, and access controls.\nDevelop reliable orchestration, CI/CD, automated testing, observability, and incident-response practices for data workloads.\nTroubleshoot pipeline failures, data delays, duplication, schema changes, and performance bottlenecks.\nCollaborate with data scientists to operationalize machine learning workflows and ensure reproducible data inputs.\nDocument data models, interfaces, operational procedures, and architectural decisions.\nMandatory Technical Skills\nStrong programming skills in Python and advanced SQL.\nHands-on experience with Apache Kafka or a managed Kafka service in production environments.\nStrong expertise in Google BigQuery, including data modeling, query optimization, partitioning, clustering, and cost management.\nPractical knowledge of ETL/ELT patterns, dimensional modeling, data lakes, data warehouses, and distributed processing.\nExperience building data pipelines for AI and machine learning workloads.\nKnowledge of machine learning lifecycle concepts, feature engineering, model inputs and outputs, and production monitoring.\nExperience with Google Cloud data services and cloud-native security practices.\nProficiency with workflow orchestration, version control, automated testing, and CI/CD.\nUnderstanding of data governance, privacy, access management, encryption, and audit requirements.\nStrong diagnostic and performance-tuning skills across streaming and analytical workloads.\nPreferred Skills\nSkill Requirements\nOther Requirements","description_format":"text","description_chars":4482,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["IT Consulting & Digital Transformation","IT Outsourcing & Dedicated Teams","Systems Integrators"],"lifecycle":[{"event":"open","at":"2026-10-07T10:19:21Z"}],"visa":[],"liveness":{"score":94,"band":"hot","label":"Hiring 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