Lead Data Engineer

Mphasis; Hyderabad; Sur place; INR 3 000 000 - 5 000 000; Plein temps

Résumé du poste

Mphasis is seeking a Senior DB Data Modeller to translate complex business requirements into scalable, governed data models. You will develop conceptual, logical, physical, dimensional, canonical and semantic models for operational, analytical and AI-ready platforms in the Insurance and Benefits Management domain.

The role requires strong SQL, data profiling, and collaboration with architects, data owners and BI teams to ensure data quality, governance and actionable analytics across enterprise

Qualifications

• 10+ years of experience in data modelling, database design, data warehousing or data architecture.
• Proven ability to deliver conceptual, logical and physical models for complex enterprise solutions.
• Strong SQL, data profiling, mapping, and relational design experience.

Responsabilités

• Create and maintain conceptual, logical and physical data models for transactional databases and data warehouses.
• Design normalized relational models and dimensional schemas for analytics and BI use cases.
• Define canonical models for benefits, employer, client, plan and policy data.
• Collaborate with business analysts and architects to identify entities, attributes, keys and rules.
• Develop source-to-target mappings and transformation rules for ETL/ELT and APIs.
• Perform data profiling and SQL analysis to validate structures and quality.
• Maintain data dictionaries, lineage and governance through catalogues.
• Review models with stakeholders; resolve duplication and semantic conflicts.
• Support indexing, partitioning, performance tuning and release impact analysis.

Connaissances

Data modelling

Relational modelling

Dimensional modelling

SQL

ETL/ELT

Azure Databricks

Delta Lake

Power BI semantic models

Data profiling

Agile

Formation

Bachelor’s degree in Computer Science / IT / Data Engineering

Outils

ERwin

ER/Studio

PowerDesigner

Visio

Git

Jira

Azure DevOps

Description du poste

Job Description:

Role Description

JOB DESCRIPTION - DB DATA MODELLER

Conceptual | Logical | Physical | Dimensional | Semantic Data Modelling

Experience

10+ years

Role Level

Senior DB Data Modeller

Primary Focus

Enterprise and Analytical Data Models

Domain

Insurance and Benefits Management

Role Summary

Seeking a Senior DB Data Modeller with 10+ years of experience translating complex business requirements into scalable, governed and implementation-ready data models. The role will develop conceptual, logical, physical, dimensional, canonical and semantic models for operational, analytical and AI-ready platforms, supported by Insurance and Benefits Management domain experience.

Key Responsibilities

• Create and maintain conceptual, logical and physical data models for transactional databases, data warehouses and Lakehouse platforms.
• Design normalized relational models and dimensional star/snowflake schemas for operational reporting, analytics, BI and AI use cases.
• Define canonical models for benefits, employer, client, plan, member, enrollment, policy, premium, claims, renewal and carrier data.
• Work with business analysts, data owners and architects to identify entities, attributes, relationships, cardinality, keys and business rules.
• Develop source-to-target mappings, transformation rules and model specifications for ETL/ELT and API integration teams.
• Perform data profiling and SQL analysis to validate source structures, model assumptions, referential integrity and data quality.
• Define naming standards, data types, domains, reference data, slowly changing dimensions, effective dating and history patterns.
• Maintain data dictionaries, model metadata, business definitions, lineage and traceability through governed repositories and catalogues.
• Conduct model reviews with architects, DBAs, engineers, BI teams and business stakeholders; resolve duplication and semantic conflicts.
• Support physical implementation, indexing, partitioning, query-performance tuning, model versioning, release impact analysis and production issue resolution.
  

Mandatory Technical Skills

Conceptual, logical and physical data modelling; relational and dimensional modelling; normalization and denormalization; star and snowflake schemas; entity-relationship modelling; canonical and semantic models; master and reference data; data profiling and source-to-target mapping; advanced SQL; PostgreSQL, SQL Server, Oracle or comparable relational databases; data-warehouse and Lakehouse modelling; Delta Lake and Azure Databricks concepts; Power BI semantic models; metadata, lineage and business glossary; data quality and reconciliation; model versioning and change control; ERwin, ER/Studio, SAP PowerDesigner, Visio or comparable modelling tools; Git, Jira or Azure DevOps; Agile delivery practices.

Experience And Qualifications

• 10+ years of experience in data modelling, database design, data warehousing, data architecture or enterprise data management.
• Proven experience delivering conceptual, logical and physical models for complex enterprise and analytical solutions.
• Strong hands‑on SQL, data profiling, mapping, dimensional modelling and relational database‑design experience.
• Experience collaborating with business, architecture, database, data‑engineering, BI, governance and testing teams.
• Experience implementing modelling standards, model governance, documentation and design‑review controls.
• Bachelor’s degree in Computer Science, Information Technology, Data Engineering or a related discipline; relevant data‑modelling certification is preferred.
  

Domain Experience

• Insurance or employee‑benefits experience across employer, client, plan, member, eligibility, enrollment, policy, carrier, premium, claims and renewal data.
• Understanding of sensitive and regulated information, employer‑level isolation, effective dating, auditability and governed metric definitions.
• Experience modelling canonical benefits data, certified metrics and semantic structures for role‑based BI or natural‑language analytics is advantageous.
  

Core Competencies

Structured data thinking, analytical problem‑solving, business‑to‑technical translation, model precision, documentation discipline, stakeholder facilitation and the ability to balance consistency, performance, scalability, governance and implementation practicality.