Data Modeller (Enterprise) at Staffnixcom · Mumbai, Bengaluru (Bangalore), Hyderabad, Gurugram · 8 - 14 years · ₹37L - ₹45L / yr · Bootstrapped · Posted 19 Apr 2026

Strong Enterprise Data Modeller profile (Modern Data Platforms)
Mandatory (Experience 1) – Must have 7+ years of experience in Data Modeling or Enterprise Data Architecture, with strong hands-on expertise in designing conceptual, logical, and physical data models for enterprise data platforms
Mandatory (Experience 2) – Must have Strong hands-on experience with enterprise data modeling tools such as Erwin, ER/Studio, PowerDesigner, SQLDBM, or similar enterprise data modeling tools
Mandatory (Experience 3) – Must have Deep understanding of dimensional modeling (Kimball / Inmon methodologies), normalization techniques, and schema design for modern data warehouse environments.
Mandatory (Experience 4) – Proven experience designing data models for modern data platforms such as Snowflake, Databricks, Redshift, Dremio, or similar cloud data warehouse / lakehouse systems.
Mandatory (Experience 5) – Must have strong SQL expertise and schema design skills, with the ability to validate data model implementations and collaborate closely with data engineering teams
Mandatory (Education) – Bachelor’s or Master’s degree in Computer Science, Information Systems, or a related technical field.
Mandatory (Note) – Total experience should not be greater than 14 years

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Location: Bangalore/Gurugram/Pune
Required Skills
- Hands-on experience in conceptual, logical, and physical data modeling.
- Strong knowledge of ER modeling, dimensional modeling, star/snowflake schemas, fact/dimension tables, and SCDs.
- Strong SQL skills for data profiling, validation, and quality checks.
- Experience with source-to-target mapping, data dictionaries, metadata, and data lineage.
- Proficiency in Erwin, ER/Studio, or similar modeling tools.
- Exposure to Snowflake, Databricks, Redshift, SQL Server, or other data platforms.
- Understanding of data governance, UAT, reconciliation, and Agile methodologies.
- Strong analytical, documentation, and stakeholder management skills.
Preferred Qualifications
- Experience in enterprise data warehousing or lakehouse modeling.
- Knowledge of data products, data contracts, quality SLAs, and reusable datasets.
- Exposure to dbt, data cataloging, metadata management, and governance tools.
- Ability to work independently in a fast-paced environment.
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Job Title: Senior Data Architect
Experience: 7+ Years
Location: India (Ahmedabad / Hybrid/Remote)
Job Summary
We are looking for an experienced Senior Data Architect to support the modernization and enhancement of Enterprise Analytic Data Warehouse (ADW). The candidate will be responsible for evaluating existing data architecture, designing scalable data models, assessing data integration across clinical, operational and financial domains, and establishing data governance and data quality standards.
Key Responsibilities
- Assess and review the existing Enterprise Analytic Data Warehouse architecture, database structures and data integration processes.
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- Define data architecture standards, database structures, data relationships and integration strategies.
- Evaluate data flows between source systems and the data warehouse.
- Design and review source-to-target mappings and data lineage.
- Identify architectural gaps affecting data completeness, consistency, integrity and reporting accuracy.
- Establish standardized data definitions, data governance practices and metadata management.
- Ensure consistent patient, facility, payer and other critical identifiers across different datasets.
- Support integration of billing, claims, payment, denial and accounts receivable (AR) data.
- Work with SQL and Python-based data processing solutions to investigate and validate data structures.
- Recommend improvements to warehouse scalability, performance, reliability and maintainability.
- Develop a prioritized architecture and modernization roadmap for future implementation.
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Required Technical Skills
- Strong experience in Data Architecture and Enterprise Data Warehousing.
- Advanced SQL and relational database concepts.
- Experience with data modelling (conceptual, logical and physical).
- Knowledge of ETL/ELT pipelines and data integration.
- Experience with data profiling, data lineage and metadata management.
- Understanding of data governance, data quality and data validation frameworks.
- Experience working with large and complex datasets.
- Python knowledge for data analysis and validation.
- Experience with cloud-based data platforms or modern data warehouse technologies.
Preferred Skills
- Experience with Databricks, Azure, AWS or similar cloud data platforms.
- Experience with healthcare data warehouses.
- Knowledge of clinical, financial, billing and claims datasets.
- Understanding of HIPAA, PHI and healthcare data security principles.
- Experience with healthcare analytics, reimbursement and revenue-cycle data.
Data Architect – Databricks & AWS
- Strong experience in Data Architecture, Data Engineering, Databricks, Apache Spark/PySpark, Python, and Advanced SQL.
- Design and implement scalable ETL/ELT pipelines, data platforms, and Lakehouse architectures using Medallion Architecture.
- Experience with Databricks, Databricks Workflows, Unity Catalog OR Databricks Jobs
- Strong knowledge of Delta Lake, Databricks Workflows, Delta Live Tables (DLT), and dimensional data modeling.
- Hands-on experience with AWS services such as S3, Glue, IAM, Lambda, and CloudWatch.
- Experience with Apache Airflow, Data Warehouse concepts, Git/CI-CD, performance optimization, and data quality.
- Good to have exposure to Kafka/Structured Streaming, Unity Catalog, and modern data governance.
About the Role
You'll be at the forefront of designing and implementing robust data platform solutions that power advanced analytics, AI, and machine learning. Working with modern cloud technologies, you'll build scalable data foundations that enable clients to make smarter, data-driven decisions.
Key Responsibilities
- Build scalable data pipelines using Snowflake, AWS, GCP, and Databricks.
- Design and optimize data models for AI and machine learning workloads.
- Develop reliable data foundations for MLOps, governance, and data lineage.
- Integrate data from multiple sources into modern data platforms.
- Leverage Snowpark ML and Snowflake's native AI capabilities.
- Ensure data platforms are secure, scalable, and high-performing.
What We're Looking For
- 5+ years of hands-on experience with Snowflake.
- Strong proficiency in SQL and Python.
- Experience with AWS, Azure, or GCP.
- Knowledge of cloud storage services such as S3, ADLS, or GCS.
- Strong understanding of Dimensional Modeling and Data Vault.
- Experience with Scala or Java is a plus.
Tech Stack
- Data Warehouse: Snowflake
- Programming: SQL, Python, Scala (Good to Have), Java (Good to Have)
- Cloud: AWS, Azure, GCP
- Storage: S3, ADLS, GCS
- AI/ML: Snowpark ML, MLOps
Perks & Benefits
- Public Speaking & Communication Program
- Mentoring Program with Senior Support Leads
- 360° Progress Reviews
- Weekly Learning Sessions & Guilds
- Paid Certifications
- Hackathons & Innovation Days
- Recognition & Rewards Programs
- Team Socials & Annual Offsites
- Employee Assistance Program (24/7 Wellbeing Support)
The Data People Shaping Tomorrow
Our client helps organizations unlock the power of data through modern cloud, analytics, and AI solutions. We believe in creating an environment where talented technologists can learn, innovate, and make a real impact while building cutting-edge data platforms for global clients. If you're passionate about data engineering and want to work with the latest technologies in AI, cloud, and analytics, we'd love to hear from you.
🚨 HIRING ALERT | DATA MODELLER – Property & Casualty INSURANCE / LLOYD’S MARKET
🚨🔹 Experience: 5–10 Years
🔹 Location: Delhi NCR | Pune | Bangalore
🔹 Notice Period: Only Serving/Immediate Joiners
Required Skills:
✅ P&;C Insurance; Lloyd’s Market (Mandatory)
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✅ Policy | Claims | Billing | Underwriting
✅ SQL | Data Warehouse
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🔁 Know someone suitable? Please share this opportunity

Job Title: Senior Data Engineer
Position Summary
We are seeking a highly skilled Data Engineer with strong expertise in reporting infrastructure, data modeling, and analytics platforms. This role will focus on building scalable reporting solutions, optimizing data pipelines, and enabling data-driven decision-making across the organization. The ideal candidate will have deep experience with databases, data warehousing, and complex query development. While Ruby on Rails experience is beneficial, the primary emphasis is on data engineering and reporting systems.
Key Responsibilities
- Design, develop, and maintain reporting infrastructure and data platforms.
- Build and optimize complex SQL queries, data models, and reporting datasets.
- Develop scalable ETL/ELT pipelines and data integration processes.
- Design and maintain database schemas to support analytics and reporting requirements.
- Collaborate with business stakeholders to understand reporting needs and translate them into technical solutions.
- Implement data quality, governance, and performance best practices.
- Support and enhance enterprise reporting solutions and dashboards.
- Work with large-scale datasets in cloud data warehouse environments.
- Troubleshoot reporting issues and optimize database performance.
- Partner with engineering and product teams to establish sustainable data architecture.
Must Have: Technical Skills
- PostgreSQL
- Snowflake
- Advanced SQL
- Data Modeling
- Data Warehousing
- Reporting & Analytics Infrastructure
- ETL/ELT Development
- Query Performance Tuning
Success Profile:
The ideal candidate is a data-focused engineer who enjoys working with complex datasets, designing scalable reporting solutions, and building foundational infrastructure that empowers analytics and business intelligence teams. They should be comfortable navigating databases, optimizing queries, and translating business reporting needs into robust technical implementations.
Experience Range: 5-10 years
Role Type: Individual Contributor / Senior Engineer
Primary Focus: Reporting Infrastructure, Data Engineering, Data Warehousing, and Analytics Enablement.
4 - 10 years of experience in designing and buildingarchitecting highly resilient data platforms
∙Strong knowledge of data engineering, architecture and data modeling
∙Experience in platforms like Databricks and Snowflake
∙Experience on building applications on cloud (AWS or Azure or Google Cloud)
∙Strong analytical and problem-solving skills
∙Prior experience in developing data or computation intensive (e.g. grid based) backend applications is an
advantage
∙OOP design skills with an understanding or at least personal interest towards the concepts of Functional
Programming
∙Willingness to understand and enhance other people’s code, being able to work in an environment where
developers will oversee and work on wider components also dealing with older “legacy” code
∙Strong programming skills (Java/ Scala / Python) skills with the willingness to pick up the other language if not
already mastered at a sufficient level is important
∙Spring knowledge is an advantage, but in general willingness to learn, work with and even enhance in-house
developed frameworks is a must
∙Prior experience in working with Git, Bitbucket, Jenkins, working with PR-s, using JIRA, following the Scrum Agile
methodology is an advantage
∙Prior knowledge of financial products is an advantage
∙Bachelors or Masters in any relevant field of IT/Engineering area is an advantage
Location: Hyderabad / Chennai
Experience: 5+ years
Employment type: Full-time, permanent
Work Hours: General Shift
website: www.amazech.com
Qualifications:
- B.E./B.Tech/M.E./M.Tech in Computer Science, Information Technology, Data Science, or related disciplines.
- Strong academic background with relevant industry experience in Data Engineering and Data Warehousing.
Key Responsibilities:
· Design, develop, and maintain scalable data warehouse solutions using Snowflake.
· Write, optimize, troubleshoot, and enhance Snowflake SQL queries with a focus on performance and scalability.
· Develop and support ETL processes using Talend to ensure reliable and efficient data movement.
· Collaborate with business, analytics, and application teams to enable reporting, dashboards, metrics, and data exploration capabilities.
· Perform data analysis and resolve issues across data ingestion, transformation, and reporting pipelines.
· Debug and troubleshoot Python-based data processing scripts and automation workflows.
· Implement best practices for data quality, testing, deployment, and code reviews.
· Work across UI, API, and Data Warehouse layers to support end-to-end data integration and business requirements.
· Monitor, optimize, and maintain data warehouse performance and operational stability.
· Create and maintain technical documentation, data models, and process workflows.
Required Skills and Experience:
· Strong hands-on expertise in Snowflake Data Warehouse.
· Advanced SQL skills with experience handling large-scale datasets.
· Strong understanding of Data Warehousing concepts, dimensional modelling, and data architecture.
· Hands-on experience with Analytical SQL functions, query tuning, and performance optimization.
· Experience developing and maintaining ETL solutions using Talend.
· Proficiency in Python for scripting, debugging, automation, and data processing.
· Experience integrating UI, API, and Data Warehouse workflows.
· Strong problem-solving and analytical skills.
· Experience with testing, code reviews, and deployment best practices.
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SENIOR DATA ENGINEER – DATABRICKS & SNOWFLAKE
Experience: 7+ Years
Location: Remote
Employment Type: Full-Time
Budget: Up to ₹30 LPA
Time Zone: US Eastern Time (ET)
Shift: US ET Night Shift
Job Description
We are looking for an experienced Senior Data Engineer with strong expertise in Databricks, Snowflake, Apache Spark, and AWS to design, develop, and maintain scalable data engineering solutions.
The candidate will work on integrating data from multiple sources, building modern data pipelines, developing enterprise data models, and preparing data platforms for AI/ML applications.
Experience in healthcare, scientific data, or the histopathology domain will be an added advantage.
Key Responsibilities
- Design, build, and maintain scalable data pipelines to acquire, integrate, and manage data from diverse sources such as laboratory systems, MES, clinical supply, quality systems, and external partners.
- Develop and optimize data workflows for structured and unstructured data using Python, PySpark, R, SQL, Databricks, and Snowflake.
- Design and maintain enterprise-level data models and data repositories.
- Perform ETL development, data warehousing, database design, and data integration.
- Ensure data quality, versioning, traceability, and consistency to support AI/ML readiness.
- Collaborate with stakeholders and technical teams to translate business requirements into technical solutions.
- Manage multiple projects while ensuring scalability, performance, and reliability.
Required Skills
- 7+ years of experience in Data Engineering.
- Strong hands-on experience with Databricks and Snowflake.
- Excellent knowledge of Apache Spark / PySpark.
- Strong experience with AWS cloud services and AWS RDS.
- Experience with Oracle and Microsoft SQL Server (MSSQL).
- Proficiency in Python, R, and SQL.
- Strong understanding of ETL, Data Warehousing (DWH), data modeling, and database design.
- Experience with dbt and cloud-based data architectures.
- Knowledge of NoSQL and graph databases.
- Strong analytical, problem-solving, and stakeholder-management skills.
- Good English communication skills (B2 level or above).
- Willingness to work in the US Eastern Time (ET) night shift.
Preferred / Good to Have
- Experience in histopathology, healthcare, life sciences, or scientific data environments.
- Knowledge of CDISC, HL7, FHIR, OMOP, DICOM, or other regulated data standards.
- Experience handling high-dimensional data, including imaging and sensor data.
- Familiarity with MLOps and machine learning model deployment workflows.
- Knowledge of Manufacturing Execution Systems (MES) and Laboratory Information Management Systems (LIMS).
- Exposure to knowledge graphs and ontology-driven architectures.
Education
Bachelor's degree in Engineering, Data Science, Life Sciences, Computer Science, or a related field. A master's degree is preferred.
Interested candidates can apply with their updated resumes.
About Solutionara
Solutionara is an AI-led IT Consulting and Technology Services organization delivering enterprise transformation through Cloud, Integration, Data Engineering, Digital Experience, AI, and Enterprise Applications.
Headquartered in the United States with a Global Delivery Center (GDC) in Noida, India, Solutionara partners with enterprise organizations across retail, manufacturing, distribution, consumer goods, and other industries to design, implement, and support scalable technology solutions.
As a rapidly growing consulting organization, we foster a culture of ownership, innovation, collaboration, and continuous improvement, empowering our teams to contribute beyond their immediate responsibilities while delivering exceptional value to our global clients.
Every team member is expected to think like a consultant and business problem solver—understanding client objectives, challenging assumptions when appropriate, leveraging AI responsibly, and continuously identifying opportunities to improve quality, productivity, and customer outcomes.
AI is embedded into how we work, but it is an enabler—not a replacement for expertise, critical thinking, or accountability.
Role Overview
Solutionara is seeking a Senior Data Engineer with strong data engineering and database expertise to design, build, optimize, and support enterprise-scale data solutions.
This is a hands-on engineering position requiring strong SQL, Python, ETL/ELT, database, cloud, and troubleshooting capabilities. The engineer will work closely with technical leads and client stakeholders while taking ownership of major engineering components.
Location: Noida, India – Onsite | Employment Type: Full-Time | Experience: 6–9 Years
Key Responsibilities
Data Engineering
- Design, develop, test, deploy, and support scalable ETL/ELT pipelines.
- Build ingestion and transformation solutions across databases, APIs, files, and enterprise applications.
- Develop high-quality SQL and Python-based data processing solutions.
- Implement batch and incremental data-processing patterns.
- Develop reusable engineering components and frameworks.
- Support complex data migration and transformation activities.
- Perform source-to-target mapping and technical data analysis.
- Implement pipeline logging, monitoring, exception handling, and recovery mechanisms.
Database & Platform Engineering
- Design and optimize relational database schemas and data structures.
- Analyze and improve SQL/query performance.
- Support indexing, partitioning, storage optimization, and database performance.
- Implement appropriate database security and access controls.
- Support database monitoring, availability, backup/recovery, and operational requirements in collaboration with infrastructure/platform teams.
- Troubleshoot database and pipeline performance issues.
- Contribute to cloud data-platform configuration and optimization.
Data Quality
- Implement automated validation and data-quality controls.
- Develop reconciliation processes between source and target systems.
- Identify duplicate, incomplete, inconsistent, and invalid data.
- Implement cleansing, standardization, validation, and exception-management logic.
- Work with technical and business stakeholders to understand and implement business data rules.
Technical Delivery
- Convert technical designs into reliable production solutions.
- Participate in technical design and code reviews.
- Mentor junior/mid-level engineers.
- Support estimation and technical planning.
- Troubleshoot complex production and integration issues.
- Collaborate with technical leads, application teams, DevOps, QA, BI, and client teams.
- Document technical designs, mappings, configurations, operational procedures, and engineering decisions.
Consulting & AI-Led Working
- Understand the business purpose behind assigned engineering requirements.
- Raise risks, assumptions, dependencies, and potential improvements proactively.
- Communicate effectively with client and internal stakeholders.
- Use AI-assisted tools to improve coding, testing, analysis, documentation, and troubleshooting productivity.
- Independently verify AI-generated code and recommendations before implementation.
- Identify opportunities for engineering automation and process improvement.
Required Qualifications
- 6–9 years of experience in data engineering, database engineering, data integration, or related areas.
- Advanced SQL skills.
- Strong hands-on Python experience.
- Strong ETL/ELT development experience.
- Strong relational database knowledge, such as SQL Server, PostgreSQL, Oracle, or equivalent.
- Experience with data modeling and database design.
- Experience with AWS, Azure, or another major cloud platform.
- Experience with modern data technologies such as Databricks, Snowflake, Redshift, Synapse, Glue, ADF, Airflow, dbt, or equivalent.
- Experience with performance tuning and optimization.
- Experience with data validation and reconciliation.
- Strong troubleshooting and analytical skills.
- Ability to take ownership of engineering deliverables with limited supervision.
Preferred Qualifications
- Experience working with enterprise ERP, CRM, PIM, PLM, MES, or related systems.
- Experience with manufacturing, retail, distribution, or consumer-goods data.
- Experience with CI/CD and infrastructure/platform engineering practices.
- Experience with API and event-driven integrations.
- Previous consulting or technology-services experience.
What Success Looks Like
- Pipelines and databases perform reliably at enterprise scale.
- Engineering solutions are maintainable, reusable, and well documented.
- Data issues are detected and addressed systematically.
- Junior engineers receive effective technical guidance.
- Problems are owned through resolution rather than simply escalated.
- Engineering decisions reflect both technical requirements and client business objectives.











