Data Modeller (Enterprise)

Hiring: Data Modeler

Experience: 6+ years

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.

If interested please share your cv at : snigdhaattheratebeanhr.com

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.

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.

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.
• Design and improve enterprise data models covering clinical, operational, financial and revenue-cycle information.
• 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.
• Coordinate with data engineers, analytics engineers and business stakeholders.

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.

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.

Sr. Data Engineer 

Location: Pune

Responsibilities 

· Lead the end-to-end design and development of scalable, high-performance data pipelines and ETL/ELT frameworks aligned with modern data engineering best practices. 

· Architect complex data integration workflows that bring together structured, semi-structured, and unstructured data from both cloud and on-premise sources. 

· Build robust real-time, batch, and on-demand pipelines with built-in observability—monitoring, alerting, and automated error handling. 

· Partner with analysts, data scientists, and business leaders to define and deliver reliable data models, quality frameworks, and SLAs that power key business insights. 

· Ensure optimal pipeline performance and throughput, with clearly defined SLAs and proactive alerting for data delivery or quality issues. 

· Collaborate with platform, DevOps, and architecture teams to build secure, reusable, and CI/CD-enabled data workflows that align with enterprise architecture standards. 

· Establish and enforce the best practices in source control, code reviews, testing automation, and continuous delivery for all data engineering components. 

· Lead root cause analysis (RCA) and preventive maintenance for critical data failures, ensuring minimal business impact and continuous service improvement. 

· Guide the team in establishing standards for data modeling, transformation logic, and governance, ensuring long-term maintainability and scalability. 

· Design and execute comprehensive testing strategies—unit, integration, and system testing—ensuring high data reliability and pipeline resilience. 

· Monitor and fine-tune data pipeline and query performance, optimizing for reliability, scalability, and cost-efficiency. 

· Create and maintain detailed technical documentation, including data architecture diagrams, process flows, and integration specifications for internal and external stakeholders. 

· Facilitate and lead discussions with business and operational teams to understand data requirements, prioritize initiatives, and drive data strategy forward. 

Qualifications 

· 7 to 10 years of hands-on experience in data engineering roles with a proven record of building scalable and secure data platforms. 

· Over 5 years of experience in scripting languages such as Python for data processing, automation, and ETL development. 

· 4+ years of experience with Snowflake, including performance tuning, security model design, and advanced SQL development. 

· 5+ years of experience with data integration tools such as Azure Data Factory, Fivetran, or Matillion. 

· 5+ years of experience in writing complex, highly optimized SQL queries on large datasets. 

· Proven experience integrating and managing APIs, JSON, XML, and webhooks for data acquisition. 

· Hands-on experience with cloud platforms (Azure/AWS) and orchestration tools like Apache Airflow or equivalent. 

· Experience with CI/CD pipelines, automated testing, and code versioning tools (e.g., Git). 

· Familiarity with dbt or similar transformation tools and best practices for modular transformation development. 

· Exposure to data visualization tools like Power BI for supporting downstream analytics is a plus. 

· Strong interpersonal and communication skills with the ability to lead discussions with technical and business stakeholders. 

Education 

Bachelor’s or Master’s degree in Computer Science/Information technology or related field. 

Equivalent academic and work experience can be considered. 

About Us:

The QX Impact was launched with a mission to make A.I accessible and affordable and deliver AI Products/Solutions at scale for the enterprises by bringing the power of Data, AI, and Engineering to drive digital transformation. We believe without insights; businesses will continue to face challenges to better understand their customers and even lose them. Secondly, without insights businesses won't’ be able to deliver differentiated products/services; and finally, without insights, businesses can’t achieve a new level of “Operational Excellence” is crucial to remain competitive, meeting rising customer expectations, expanding markets, and digitalization.

Job Summary:

We are looking for a Senior Data Engineer who is creative, collaborative, and adaptable to join our agile team of data scientists, engineers, and UX developers. The role focuses on building and maintaining robust data pipelines to support advanced analytics, data science, and BI solutions.

As a Senior Data Engineer, you will work with internal and external data, collaborate with data scientists, and contribute to the design, development, and deployment of innovative solutions.

Key Responsibilities:

• Design, develop, test, and maintain optimal data pipeline and ETL architectures.
• Map out data systems and define/design required integrations, ETL, BI, and AI systems/processes.
• Prepare and optimize data for predictive and prescriptive modeling.
• Collaborate with teams to integrate ERP data into the enterprise data lake, ensuring seamless flow and quality.
• Enhance cloud data infrastructure on AWS or Azure for scalability and performance.
• Utilize big data tools and frameworks to optimize data acquisition and preparation.
• Build architectures to move data to/from data lakes and data warehouses for advanced analytics.
• Develop and curate data models for analytics, dashboards, and reports.
• Conduct code reviews, maintain production-level code, and implement testing approaches.
• Monitor, troubleshoot, and resolve data ingestion workflows to maintain reliability and uptime.
• Drive innovation and implement efficient new approaches to data engineering tasks.

Must-Have Skills:

• Bachelor’s degree in Computer Science, Mathematics, Engineering, or a related field.
• 5+ years of experience working with enterprise data platforms, including building and managing data lakes.
• 3–5 years of experience designing and implementing data warehouse solutions.
• Expertise in SQL, including developing stored procedures (SP) and applying advanced data design concepts.
• Proficiency in Spark (Python/Scala) and Spark Streaming for real-time data pipelines.
• Experience with AWS or Azure services (e.g., AWS Glue, Azure Data Factory, Redshift, Snowflake).
• Familiarity with big data tools such as Apache Kafka, Apache Spark, or Flink.
• Hands-on experience with orchestration tools (e.g., Apache Airflow, Prefect).
• Knowledge of CI/CD processes, version control (e.g., Git, Jenkins), and deployment automation.
• Strong problem-solving, communication, and collaboration skills.

Good-to-Have Skills:

• Experience in integrating ERP data into data lakes.
• Experience with traditional ETL tools (e.g., Talend, Pentaho).

Competencies:

• Tech Savvy - Anticipating and adopting innovations in business-building digital and technology applications.
• Self-Development - Actively seeking new ways to grow and be challenged using both formal and informal development channels.
• Action Oriented - Taking on new opportunities and tough challenges with a sense of urgency, high energy, and enthusiasm.
• Customer Focus - Building strong customer relationships and delivering customer-centric solutions.
• Optimize Work Processes - Knowing the most effective and efficient processes to get things done, with a focus on continuous improvement.

Why Join Us?

• Be part of a collaborative and agile team driving cutting-edge AI and data engineering solutions.
• Work on impactful projects that make a difference across industries.
• Opportunities for professional growth and continuous learning.
• Competitive salary and benefits package.

Application Details

Ready to make an impact? Apply today and become part of the QX Impact team!

Responsibilities and JD

Job Description: We are looking for a Senior Developer with strong expertise in PySpark, Databricks, and Snowflake to build scalable data engineering solutions and enterprise data platforms.

Key Responsibilities:

1. Design, develop, and maintain ETL/ELT pipelines using PySpark, Databricks, and Snowflake.
2. Develop batch and real-time data processing solutions for structured and semi-structured data.
3. Build and optimize Databricks notebooks, workflows, and Delta Lake solutions.
4. Design and implement Snowflake databases, schemas, views, stored procedures, tasks, and streams.
5. Develop scalable data models, data marts, and data warehouse solutions.
6. Optimize PySpark jobs, Databricks workloads, and Snowflake queries for performance and cost efficiency.
7. Implement data quality, validation, governance, and security controls.
8. Collaborate with business stakeholders, architects, and cross-functional teams to deliver data solutions.
9. Manage source control and CI/CD deployments using Git and Azure DevOps.
10. Troubleshoot production issues, perform root cause analysis, and ensure pipeline reliability.
11. Mentor junior team members and participate in code reviews and technical design discussions.

Required Skills: PySpark, Databricks, Snowflake, Python, SQL.

Experience: 5+ years of Data Engineering experience with strong hands-on expertise in PySpark, Databricks, and Snowflake.

Job Description: Associate Director-Data Engineering (Databricks Lakehouse Architect + Cloud Data Engineering Leader)

About our Client -

Client is a leading SAP consulting and digital transformation partner committed to delivering innovative, high-impact solutions across enterprise analytics, cloud, and intelligent business platforms.

As enterprises modernize their data landscapes, the role of traditional data warehousing is rapidly evolving into scalable cloud-native Data Lakehouse architectures. Applexus is expanding its modern data engineering capabilities to help customers integrate enterprise data from SAP and non-SAP ecosystems, build advanced analytics platforms, and enable future-ready AI-driven solutions.

We are looking for an accomplished Databricks Lakehouse Architect / Cloud Data Engineering Leader who can play a key role in this transformation journey — designing modern data platforms, leading cloud data engineering initiatives, and bridging enterprise analytics with next-generation technologies.

The ideal candidate will combine strong hands-on expertise in Databricks, Spark/PySpark, Delta Lake, and Azure/AWS cloud platforms with the ability to engage customers, architect solutions, guide engineering teams, and drive successful delivery.

Role Overview:

We are looking for a hands-on Data Engineering leader who can architect modern cloud data platforms using Databricks Lakehouse and lead solution design, delivery, and engineering teams. This role requires strong technical ownership rather than a pure people management background.

Key Responsibilities:

• Architect scalable Data Lakehouse platforms

• Own solution design, estimation, architecture reviews and client discussions

• Build and optimize enterprise data pipelines

• Provide technical guidance to data engineering teams

• Establish best practices around performance, governance and architecture

Must Have - Databricks Architecture:

• Databricks Lakehouse design

• Delta Lake implementation

• Apache Spark / PySpark engineering

• Medallion architecture (Bronze/Silver/Gold layers)

• Data pipeline optimization

• Unity Catalog and data governance exposure

Cloud Platform Expertise (depth in one required):

Microsoft Azure:

• Azure Databricks

• Azure Data Factory (ADF)

• Azure Data Lake Storage (ADLS)

• Azure Synapse Analytics

OR

Amazon AWS:

• Databricks on AWS

• Amazon S3

• AWS Glue

• Redshift

• Athena

Good to Have:

• Snowflake

• ML Engineering exposure

• Cloud/Data Engineering certifications

Ideal Candidate:

A hands-on architect who can talk to customers, design solutions, estimate projects, guide engineers and review architecture while helping build a modern Data Engineering practice.

 We are looking for an accomplished Databricks Lakehouse Architect / Cloud Data Engineering Leader who can play a key role in this transformation journey — designing modern data platforms, leading cloud data engineering initiatives, and bridging enterprise analytics with next-generation technologies.

The ideal candidate will combine strong hands-on expertise in Databricks, Spark/PySpark, Delta Lake, and Azure/AWS cloud platforms with the ability to engage customers, architect solutions, guide engineering teams, and drive successful delivery.

If you are a hands-on Data Architect who enjoys building modern data platforms and wants to move beyond execution into solution ownership and leadership, I would love to connect.

Job Overview:

We are seeking an experienced Cloud Data Engineer to the architecture, development, Data migration, ETL pipeline in cloud and optimization of a next-generation data platform. This is a development role for an individual with over 4 years of deep data engineering and data migration, data pipeline in AWS expertise, capable of driving technical direction, and delivering high-impact solutions in a fast-paced

4+ Years of hands-on, progressive experience in Cloud Data Engineering, cloud ecosystems AWS, Data Architecture

• Deep conceptual understanding of core data engineering principles, ETL/ELT patterns, and metadata management

•  Ability to translate complex business needs into actionable technical requirements.

•  Strong troubleshooting and problem-solving skills in distributed systems.

•  Agile/Scrum teamwork capabilities and version control practices

• Insurance industry experience preferred but not mandatory

• Tools:

o Cloud Environment: AWS (S3, IAM, VPC, Glue, Lambda etc.)

o Experience with dbt Core, Iceberg, AWS Glue Catalog, AWS lambda, Redshift, Athena, Splunk, AmazonQ, AWS streaming services, Git

o Strong SQL, Pyspark and Python