[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"seo-content-expertise-data-pipelines-in":3,"job-search-\u002Fen-in\u002Fjobs-by-expertise\u002Fdata-pipelines":20,"expertise-filters-\u002Fen-in\u002Fjobs-by-expertise\u002Fdata-pipelines":297,"featured-jobs-\u002Fen-in\u002Fjobs-by-expertise\u002Fdata-pipelines":444},{"image_path":4,"content_de":4,"content_en":5,"composite_key":18,"last_updated":19},null,{"tab1":6,"tab2":9,"tab3":12,"tab4":15},{"title":7,"content":8},"Strategic Importance","In the rapidly evolving financial landscape of Indien, Data Pipelines have become the backbone of data-driven decision-making. As financial institutions generate and consume vast amounts of data, the ability to efficiently collect, process, and deliver this data is critical. Data Pipelines enable real-time analytics, regulatory reporting, and customer insights, directly impacting operational efficiency and competitive advantage.\n\nProfessionals with expertise in Data Pipelines are in high demand, and this role often intersects with other key areas such as [Mlops](\u002Fen-in\u002Fjobs-by-expertise\u002Fmlops), [Generative Ai](\u002Fen-in\u002Fjobs-by-expertise\u002Fgenerative-ai), [Devsecops](\u002Fen-in\u002Fjobs-by-expertise\u002Fdevsecops). The synergy between these skills allows organizations to build robust data ecosystems that support risk management, compliance, and strategic planning. In Indien, where the financial sector is embracing digital transformation, the importance of Data Pipelines cannot be overstated. They are not just a technical necessity but a strategic asset that drives innovation and growth.\n\nFor professionals, mastering Data Pipelines opens doors to impactful roles in banking, fintech, and beyond. The ability to design and maintain efficient data flows is a differentiator in a competitive job market, making it a career-defining skill.",{"title":10,"content":11},"Top Industries & Locations","Data Pipelines expertise is particularly sought after in Indien's leading financial hubs. The top locations for these roles include [Hyderabad](\u002Fen-in\u002Fjobs-in-hyderabad), [Mumbai](\u002Fen-in\u002Fjobs-in-mumbai), [Bengaluru](\u002Fen-in\u002Fjobs-in-bengaluru), where major banks, insurance companies, and fintech firms are concentrated. These cities offer a dynamic environment for data professionals, with numerous opportunities to work on cutting-edge projects.\n\nIn terms of industries, [Insurance](\u002Fen-in\u002Fjobs-by-industry\u002Finsurance), [Financial Services](\u002Fen-in\u002Fjobs-by-industry\u002Ffinancial-services), [Financial Markets Infrastructure](\u002Fen-in\u002Fjobs-by-industry\u002Ffinancial-markets-infrastructure) are at the forefront of adopting advanced data pipeline solutions. Banking and financial services rely on these pipelines for transaction processing and fraud detection, while insurance companies use them for risk assessment and claims management. The fintech sector, with its agile and data-centric approach, is a major driver of demand for Data Pipeline experts.\n\nAs Indien continues to solidify its position as a global financial hub, the need for skilled data professionals will only grow. Professionals with expertise in Data Pipelines can expect a wealth of opportunities across these industries and locations, making it a strategic career choice.",{"title":13,"content":14},"In-Demand Skills & Employers","To excel in Data Pipelines roles in Indien, professionals must possess a blend of technical and analytical skills. The most sought-after skills include [Python](\u002Fen-in\u002Fjobs-by-skill\u002Fpython), [Pytorch](\u002Fen-in\u002Fjobs-by-skill\u002Fpytorch), [Ci\u002Fcd](\u002Fen-in\u002Fjobs-by-skill\u002Fcicd), which are essential for building and managing efficient data flows. Proficiency in these tools allows professionals to design pipelines that are scalable, reliable, and secure.\n\nLeading employers in this space include [Hartford Global Services Private Limited](\u002Fen-in\u002Fcompany\u002Fhartford-global-services-private-limited), [M&G Global Services](\u002Fen-in\u002Fcompany\u002Fmg-global-services), [Refinitiv India Shared Services Private Limited](\u002Fen-in\u002Fcompany\u002Frefinitiv-india-shared-services-private-limited), which are known for their innovative use of data. These organizations offer challenging roles that require not only technical expertise but also a deep understanding of financial processes. Working for such employers provides exposure to large-scale data environments and the opportunity to work with cross-functional teams.\n\nFor job seekers, highlighting these skills and targeting these employers can significantly boost career prospects. The demand for Data Pipeline professionals is high, and those who continuously update their skills will find themselves in a strong position in the Indien job market.",{"title":16,"content":17},"Career Path & Salary","A career in Data Pipelines in Indien offers a rewarding trajectory with competitive compensation. Currently, there are 6 open positions, reflecting the strong demand for these skills. The median salary for Data Pipeline professionals is INR 4250k, with a typical range of INR 3250k – INR 5000k. Entry-level positions start at INR 1500k, while experienced professionals can earn up to INR 5000k.\n\nCareer progression often follows a path from Data Engineer to Senior Data Engineer, and eventually to Data Architect or Analytics Manager. With the right experience and continuous learning, professionals can move into leadership roles that shape an organization's data strategy.\n\nThe salary figures indicate the value that employers place on Data Pipeline expertise. As data continues to drive business decisions, the demand for skilled professionals is expected to rise, making this a lucrative and stable career choice in Indien.","expertise:data-pipelines:in","2026-08-17T20:39:03.610627+02:00",{"total":21,"jobs":22},6,[23,78,119,185,243,277],{"id":24,"slug":25,"title":26,"raw_title":4,"is_featured":27,"company":28,"raw_hiringOrganization_logo_url":4,"processed_city_gmaps":31,"processed_country_iso_code_gmaps":33,"processed_home_office":35,"processed_salary_min":36,"processed_salary_max":37,"processed_salary_currency":38,"processed_salary_source":39,"processed_working_hours":40,"processed_employment_types":42,"processed_it_skills":43,"processed_it_skills_labels":46,"processed_job_expertise_skills":64,"processed_job_expertise_skills_labels":65,"max_cpc":77,"actual_cpc":77},88186,"ind-senior-engineer-data-hartford-global-services-private-limited-2","IND Senior Engineer, Data",false,{"name":29,"slug":30,"logo_url":4},"Hartford Global Services Private Limited","hartford-global-services-private-limited",[32],"Hyderabad",[34],"IN","onsite",1500000,2500000,"INR","estimated",[41],"FULL_TIME","permanent",{"adk":44,"aws":44,"etl":45,"sql":45,"ci\u002Fcd":44,"python":45,"harness":44,"jenkins":44,"tableau":44,"bigquery":44,"snowflake":45,"terraform":44,"vertex ai":44,"informatica":44,"thoughtspot":44,"cloud functions":44,"google cloud platform":44},3,4,[47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63],"Snowflake","SQL","Python","AWS","Google Cloud Platform","BigQuery","Cloud Functions","Vertex AI","Jenkins","Terraform","Harness","ADK","ThoughtSpot","Tableau","Informatica","ETL","CI\u002FCD",{"agentic ai":44,"data quality":45,"etl processes":45,"generative ai":44,"data pipelines":45,"data engineering":45,"data integration":45,"data orchestration":44,"data visualization":44,"prompt engineering":44,"model orchestration":44},[66,67,68,69,70,71,72,73,74,75,76],"Data Engineering","Data Pipelines","ETL Processes","Data Integration","Data Quality","Data Visualization","Generative AI","Prompt Engineering","Agentic AI","Model Orchestration","Data Orchestration",1,{"id":79,"slug":80,"title":81,"raw_title":4,"is_featured":27,"company":82,"raw_hiringOrganization_logo_url":4,"processed_city_gmaps":86,"processed_country_iso_code_gmaps":88,"processed_home_office":89,"processed_salary_min":90,"processed_salary_max":91,"processed_salary_currency":92,"processed_salary_source":39,"processed_working_hours":93,"processed_employment_types":42,"processed_it_skills":94,"processed_it_skills_labels":96,"processed_job_expertise_skills":111,"processed_job_expertise_skills_labels":112,"max_cpc":77,"actual_cpc":77},81733,"consultant-business-analytics-allianz-insurance-2","Consultant-Business Analytics",{"name":83,"slug":84,"logo_url":85},"Allianz Insurance","allianz-insurance","https:\u002F\u002Fcdn.phenompeople.com\u002FCareerConnectResources\u002FAISAIPGB\u002Fimages\u002FHeader-1706868786965.svg",[87],"PUNE",[34],"hybrid",50000,80000,"GBP",[41],{"sql":45,"ci\u002Fcd":44,"mlops":95,"spark":45,"github":44,"python":45,"etl\u002Felt":45,"pyspark":45,"databricks":45,"delta lake":45,"azure synapse":45,"unity catalog":45,"data lakehouse":44,"data modelling":44,"machine learning":95,"feature engineering":95,"distributed data processing":44},2,[97,98,99,100,101,102,48,49,103,104,63,105,106,107,108,109,110],"Azure Synapse","Databricks","Spark","PySpark","Delta Lake","Unity Catalog","ETL\u002FELT","GitHub","Data Modelling","Data Lakehouse","Distributed Data Processing","MLOps","Feature Engineering","Machine Learning",{"mlops":95,"data security":44,"data pipelines":45,"data governance":44,"data engineering":45,"feature engineering":95,"analytics engineering":45,"data privacy compliance":44,"insurance market knowledge":95,"model lifecycle management":95},[66,113,67,114,115,116,117,108,109,118],"Analytics Engineering","Data Governance","Data Security","Data Privacy Compliance","Insurance Market Knowledge","Model Lifecycle Management",{"id":120,"slug":121,"title":122,"raw_title":4,"is_featured":27,"company":123,"raw_hiringOrganization_logo_url":4,"processed_city_gmaps":126,"processed_country_iso_code_gmaps":128,"processed_home_office":89,"processed_salary_min":129,"processed_salary_max":130,"processed_salary_currency":38,"processed_salary_source":39,"processed_working_hours":131,"processed_employment_types":42,"processed_it_skills":132,"processed_it_skills_labels":133,"processed_job_expertise_skills":154,"processed_job_expertise_skills_labels":155,"max_cpc":77,"actual_cpc":77},75310,"senior-lead-ai-engineer-mg-global-services","Senior Lead AI Engineer",{"name":124,"slug":125,"logo_url":4},"M&G Global Services","mg-global-services",[127],"Mumbai",[34],3000000,5000000,[41],{"java":44,"jira":44,"docker":45,"python":45,"pytorch":45,"checkmarx":44,"langchain":45,"sonarqube":44,"confluence":44,"kubernetes":45,"tensorflow":45,"azure devops":45,"azure openai":45,"hugging face":45,"power platform":95,"microsoft azure":45,"microsoft teams":44,"semantic kernel":45,"microsoft purview":44,"databricks unity catalog":44,"microsoft copilot studio":95},[49,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153],"Java","Azure OpenAI","LangChain","Semantic Kernel","Hugging Face","PyTorch","TensorFlow","Docker","Kubernetes","Azure DevOps","Microsoft Azure","SonarQube","Checkmarx","Microsoft Teams","Jira","Confluence","Microsoft Purview","Databricks Unity Catalog","Power Platform","Microsoft Copilot Studio",{"mlops":45,"scrum":45,"kanban":45,"security":45,"devsecops":45,"embeddings":45,"ai governance":45,"generative ai":45,"microservices":45,"observability":45,"vector search":45,"data pipelines":45,"responsible ai":45,"api development":45,"ci\u002Fcd pipelines":45,"data governance":45,"design patterns":45,"drift detection":45,"data cataloguing":45,"machine learning":45,"model monitoring":45,"vendor management":44,"performance tuning":45,"prompt engineering":45,"agile methodologies":45,"ai solution delivery":45,"software architecture":45,"infrastructure-as-code":45,"large language models (llms)":45,"regulated environment experience":45,"retrieval-augmented generation (rag)":45},[156,157,72,158,159,160,161,162,163,164,165,166,167,168,169,170,108,171,172,173,174,175,176,177,178,179,180,181,182,183,184],"AI solution delivery","Machine learning","Large Language Models (LLMs)","Prompt engineering","Embeddings","Vector search","Retrieval-Augmented Generation (RAG)","Agile methodologies","Scrum","Kanban","DevSecOps","CI\u002FCD pipelines","API development","Microservices","Data pipelines","Model monitoring","Drift detection","Responsible AI","AI governance","Security","Observability","Infrastructure-as-code","Software architecture","Design patterns","Performance tuning","Regulated environment experience","Data governance","Data cataloguing","Vendor management",{"id":186,"slug":187,"title":188,"raw_title":4,"is_featured":27,"company":189,"raw_hiringOrganization_logo_url":4,"processed_city_gmaps":190,"processed_country_iso_code_gmaps":191,"processed_home_office":35,"processed_salary_min":192,"processed_salary_max":193,"processed_salary_currency":38,"processed_salary_source":39,"processed_working_hours":194,"processed_employment_types":42,"processed_it_skills":195,"processed_it_skills_labels":196,"processed_job_expertise_skills":214,"processed_job_expertise_skills_labels":215,"max_cpc":77,"actual_cpc":77},78804,"ind-lead-software-engineer-hartford-global-services-private-limited-4","IND Lead Software Engineer",{"name":29,"slug":30,"logo_url":4},[32],[34],2000000,3500000,[41],{"a2a":44,"adk":44,"mcp":44,"nexus":95,"numpy":44,"crewai":44,"github":44,"pandas":44,"python":45,"airflow":95,"asyncio":44,"fastapi":44,"pytorch":44,"mcp-scan":95,"pydantic":44,"checkmarx":95,"langchain":44,"langgraph":44,"snowflake":95,"sonarqube":95,"terraform":44,"aws bedrock":44,"hugging face":44,"aws sagemaker":44,"opentelemetry":44,"apache iceberg":95,"google vertex ai":44},[49,139,138,197,198,199,200,201,56,104,202,145,146,203,47,204,205,206,207,208,209,136,210,211,58,212,213],"Pandas","NumPy","AWS SageMaker","AWS Bedrock","Google Vertex AI","Nexus","mcp-scan","Airflow","Apache Iceberg","OpenTelemetry","FastAPI","Pydantic","asyncio","LangGraph","CrewAI","A2A","MCP",{"hyde":44,"safe":44,"ci\u002Fcd":44,"mlops":45,"devops":44,"raptor":44,"pub\u002Fsub":44,"graphrag":44,"devsecops":44,"agentic ai":45,"agentic rag":45,"rag systems":45,"generative ai":45,"microservices":44,"observability":44,"data pipelines":95,"ml engineering":45,"api development":44,"bias mitigation":95,"drift monitoring":44,"llm architecture":45,"model evaluation":44,"automated testing":95,"adaptive prompting":44,"canary deployments":95,"agile methodologies":44,"context engineering":44,"distributed systems":44,"rollback strategies":95,"counterfactual testing":95,"infrastructure as code":44,"multi-agent coordination":44,"disparate impact analysis":95,"event-driven architecture":44,"point-to-point architecture":44},[216,108,217,72,74,218,219,220,221,222,223,224,225,226,63,227,166,228,229,230,231,232,233,234,169,235,176,236,237,238,239,67,240,241,242],"ML Engineering","LLM Architecture","RAG Systems","Agentic RAG","Context Engineering","Adaptive Prompting","Multi-agent Coordination","HyDE","RAPTOR","GraphRAG","DevOps","Infrastructure as Code","Agile Methodologies","SAFe","Distributed Systems","Event-driven Architecture","Pub\u002FSub","Point-to-point Architecture","API Development","Model Evaluation","Drift Monitoring","Automated Testing","Canary Deployments","Rollback Strategies","Bias Mitigation","Disparate Impact Analysis","Counterfactual Testing",{"id":244,"slug":245,"title":246,"raw_title":4,"is_featured":27,"company":247,"raw_hiringOrganization_logo_url":4,"processed_city_gmaps":250,"processed_country_iso_code_gmaps":252,"processed_home_office":35,"processed_salary_min":253,"processed_salary_max":254,"processed_salary_currency":255,"processed_salary_source":39,"processed_working_hours":256,"processed_employment_types":42,"processed_it_skills":257,"processed_it_skills_labels":258,"processed_job_expertise_skills":262,"processed_job_expertise_skills_labels":263,"max_cpc":77,"actual_cpc":77},59767,"lead-data-scientist-ind201-refinitiv-india-shared-services-private-limited-2","Lead Data Scientist",{"name":248,"slug":249,"logo_url":4},"Refinitiv India Shared Services Private Limited","refinitiv-india-shared-services-private-limited",[251],"Bengaluru",[34],120000,180000,"USD",[41],{"r":44,"aws":45,"sql":44,"azure":45,"ci\u002Fcd":45,"mlops":45,"python":45,"pytorch":45,"tensorflow":45,"scikit-learn":45},[49,259,48,140,139,260,50,261,108,63],"R","Scikit-learn","Azure",{"nlp":45,"rag":45,"llms":45,"statistics":45,"data science":45,"web scraping":44,"ai governance":44,"deep learning":45,"data pipelines":45,"responsible ai":44,"cloud computing":45,"data extraction":44,"data engineering":45,"machine learning":45,"model deployment":45,"model evaluation":45,"entity recognition":44,"financial analytics":44},[264,110,265,266,267,268,269,66,270,271,272,273,274,275,276,67,235,173],"Data Science","Deep Learning","NLP","LLMs","RAG","Statistics","Cloud Computing","Model Deployment","AI Governance","Financial Analytics","Web Scraping","Entity Recognition","Data Extraction",{"id":278,"slug":279,"title":280,"raw_title":4,"is_featured":27,"company":281,"raw_hiringOrganization_logo_url":4,"processed_city_gmaps":282,"processed_country_iso_code_gmaps":283,"processed_home_office":89,"processed_salary_min":129,"processed_salary_max":130,"processed_salary_currency":38,"processed_salary_source":39,"processed_working_hours":284,"processed_employment_types":42,"processed_it_skills":285,"processed_it_skills_labels":286,"processed_job_expertise_skills":291,"processed_job_expertise_skills_labels":292,"max_cpc":77,"actual_cpc":296},51724,"ind-staff-software-engineer-hartford-global-services-private-limited-3","IND Staff Software Engineer",{"name":29,"slug":30,"logo_url":4},[32],[34],[41],{"s3":44,"a2a":45,"adk":45,"emr":44,"mcp":45,"glue":44,"ci\u002Fcd":44,"nexus":44,"numpy":45,"crewai":45,"docker":44,"github":44,"pandas":45,"python":45,"airflow":44,"fastapi":45,"pytorch":45,"mcp-scan":44,"pydantic":45,"redshift":44,"checkmarx":44,"langchain":45,"langgraph":45,"snowflake":44,"sonarqube":44,"terraform":44,"kubernetes":44,"aws bedrock":45,"hugging face":45,"aws sagemaker":45,"opentelemetry":44,"apache iceberg":44,"google vertex ai":45},[49,139,138,197,198,207,208,206,56,104,199,200,201,136,210,211,58,212,213,141,142,63,202,145,146,203,47,204,287,288,289,290,205],"S3","Glue","EMR","Redshift",{"hyde":45,"safe":45,"mlops":45,"devops":45,"raptor":45,"pub\u002Fsub":45,"graphrag":45,"devsecops":45,"agentic ai":45,"agentic rag":45,"rag systems":45,"generative ai":45,"data pipelines":44,"bias mitigation":44,"drift monitoring":45,"llm architecture":45,"machine learning":45,"model evaluation":45,"adaptive prompting":45,"agile methodologies":45,"context engineering":45,"distributed systems":45,"counterfactual testing":44,"infrastructure as code":45,"multi-agent coordination":45,"disparate impact analysis":44,"event-driven architecture":45,"point-to-point architecture":45},[110,108,217,72,74,218,219,220,221,293,223,224,225,235,236,230,294,232,295,226,166,227,228,229,67,240,241,242],"Multi-Agent Coordination","Event-Driven Architecture","Point-to-Point Architecture",0.3,{"industry":298,"employment_type":311,"expertise_skills":314,"it_skills":377,"salary_currency":438,"processed_working_hours":442},[299,302,305,308],{"key":300,"label":301,"count":45},"insurance","Insurance",{"key":303,"label":304,"count":77},"financial markets infrastructure","Financial Markets Infrastructure",{"key":306,"label":307,"count":77},"data provider","Data Provider",{"key":309,"label":310,"count":77},"financial services","Financial Services",[312],{"key":42,"label":313,"count":21},"PERMANENT",[315,317,320,323,325,327,329,331,334,337,339,341,344,347,349,351,354,357,360,362,365,368,371,374],{"key":316,"label":67,"slug":316,"count":21},"data-pipelines",{"key":318,"label":319,"slug":318,"count":45},"mlops","Mlops",{"key":321,"label":322,"slug":321,"count":45},"generative-ai","Generative Ai",{"key":324,"label":235,"slug":324,"count":44},"model-evaluation",{"key":326,"label":110,"slug":326,"count":44},"machine-learning",{"key":328,"label":66,"slug":328,"count":44},"data-engineering",{"key":330,"label":228,"slug":330,"count":44},"agile-methodologies",{"key":332,"label":333,"slug":332,"count":44},"agentic-ai","Agentic Ai",{"key":335,"label":336,"slug":335,"count":44},"devsecops","Devsecops",{"key":338,"label":236,"slug":338,"count":95},"drift-monitoring",{"key":340,"label":176,"slug":340,"count":95},"observability",{"key":342,"label":343,"slug":342,"count":95},"ai-governance","Ai Governance",{"key":345,"label":346,"slug":345,"count":95},"point-to-point-architecture","Point-To-Point Architecture",{"key":348,"label":73,"slug":348,"count":95},"prompt-engineering",{"key":350,"label":232,"slug":350,"count":95},"pubsub",{"key":352,"label":353,"slug":352,"count":95},"rag-systems","Rag Systems",{"key":355,"label":356,"slug":355,"count":95},"raptor","Raptor",{"key":358,"label":359,"slug":358,"count":95},"responsible-ai","Responsible Ai",{"key":361,"label":294,"slug":361,"count":95},"event-driven-architecture",{"key":363,"label":364,"slug":363,"count":95},"agentic-rag","Agentic Rag",{"key":366,"label":367,"slug":366,"count":95},"safe","Safe",{"key":369,"label":370,"slug":369,"count":95},"api-development","Api Development",{"key":372,"label":373,"slug":372,"count":95},"graphrag","Graphrag",{"key":375,"label":376,"slug":375,"count":95},"hyde","Hyde",[378,380,383,386,389,392,395,397,399,401,404,406,408,410,412,414,416,418,421,424,427,430,432,435],{"key":379,"label":49,"slug":379,"count":21},"python",{"key":381,"label":382,"slug":381,"count":45},"cicd","Ci\u002FCd",{"key":384,"label":385,"slug":384,"count":45},"pytorch","Pytorch",{"key":387,"label":388,"slug":387,"count":44},"langchain","Langchain",{"key":390,"label":391,"slug":390,"count":44},"github","Github",{"key":393,"label":394,"slug":393,"count":44},"adk","Adk",{"key":396,"label":138,"slug":396,"count":44},"hugging-face",{"key":398,"label":146,"slug":398,"count":44},"checkmarx",{"key":400,"label":47,"slug":400,"count":44},"snowflake",{"key":402,"label":403,"slug":402,"count":44},"sonarqube","Sonarqube",{"key":405,"label":48,"slug":405,"count":44},"sql",{"key":407,"label":56,"slug":407,"count":44},"terraform",{"key":409,"label":197,"slug":409,"count":95},"pandas",{"key":411,"label":204,"slug":411,"count":95},"airflow",{"key":413,"label":208,"slug":413,"count":95},"pydantic",{"key":415,"label":142,"slug":415,"count":95},"kubernetes",{"key":203,"label":417,"slug":203,"count":95},"Mcp-Scan",{"key":419,"label":420,"slug":419,"count":95},"mcp","Mcp",{"key":422,"label":423,"slug":422,"count":95},"aws","Aws",{"key":425,"label":426,"slug":425,"count":95},"google-vertex-ai","Google Vertex Ai",{"key":428,"label":429,"slug":428,"count":95},"aws-bedrock","Aws Bedrock",{"key":431,"label":141,"slug":431,"count":95},"docker",{"key":433,"label":434,"slug":433,"count":95},"tensorflow","Tensorflow",{"key":436,"label":437,"slug":436,"count":95},"langgraph","Langgraph",[439,440,441],{"key":38,"label":38,"count":45},{"key":92,"label":92,"count":77},{"key":255,"label":255,"count":77},[443],{"key":41,"label":41,"count":21},{"total":445,"jobs":446},0,[]]