Senior Data Modeller (Enterprise)

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.

Design, build, and maintain end-to-end data pipelines to ingest, process, and transform data from files, streams,

APIs, and relational/non-relational databases into Snowflake.   Develop and optimize ELT/ETL pipelines using Snowflake SQL,

Snowpipe, Streams & Tasks, and cloud-native orchestration tools.   Implement scalable data models and schemas (staging, curated, and consumption layers) to support analytics and reporting use cases.   Develop transformations and business logic using SQL and Python, including Snowflake UDFs and stored procedures.   Optimize Snowflake performance and cost through query tuning, warehouse sizing, clustering, and resource management.   Integrate Snowflake with cloud storage and services across AWS and Azure (e.g., object storage, data integration, and mess

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.

·       Excellent communication and stakeholder management skills.

We are looking for a Data Engineer with at least 1 year of hands-on experience building solutions on Snowflake. The candidate should be comfortable designing, building, and managing reliable data pipelines that move data from multiple sources into a central data platform.

Responsibilities

• Build and maintain data pipelines for ingesting, transforming, and loading data into Snowflake
• Design scalable data models, schemas, tables, and views in Snowflake
• Develop ETL/ELT workflows using SQL, Python, or data orchestration tools
• Integrate data from APIs, databases, files, and third-party platforms
• Monitor pipeline performance, failures, data quality, and freshness
• Optimize Snowflake queries, warehouses, storage, and compute usage
• Implement incremental loads, change data capture, and scheduled workflows
• Work with engineering and business teams to understand data requirements
• Maintain documentation for pipelines, datasets, and data transformations

Requirements

• 1+ year of hands-on experience working with Snowflake
• Strong SQL skills and experience writing complex queries
• Experience building and managing ETL or ELT data pipelines
• Knowledge of data warehousing concepts, dimensional modelling, and data quality
• Experience with Python or another scripting language
• Familiarity with orchestration tools such as Airflow, Dagster, Prefect, dbt, or similar
• Understanding of APIs, relational databases, file formats, and cloud storage
• Ability to troubleshoot pipeline failures and performance issues
• Strong analytical, problem-solving, and communication skills

Good to Have

• Experience with dbt and Snowflake Tasks, Streams, Snowpipe, or Dynamic Tables
• Knowledge of AWS, Azure, or Google Cloud
• Experience with Kafka or other streaming platforms
• Familiarity with CI/CD, Git, monitoring, and data governance practices
• Experience integrating ERP, finance, or operational systems

Role Summary:

We are looking for an experienced Snowflake Lead to lead the design, development, migration, and optimization of enterprise data platforms using Snowflake. The candidate will provide technical leadership to data engineering teams and work closely with architects, business stakeholders, and application teams.

Key Responsibilities

• Lead the architecture and development of scalable Snowflake data warehouse solutions.
• Design and develop scalable Azure Data Factory (ADF) pipelines for API-based and batch data ingestion, implementing parameterized workflows, scheduling, and error handling.
• Build and optimize enterprise Snowflake data warehouse solutions using Snowflake SQL, Streams, Tasks, Stored Procedures, VARIANT data type, and LATERAL FLATTEN for semi-structured JSON processing.
• Integrate GraphQL and REST APIs using OAuth 2.0, implementing secure API authentication, JSON parsing, and API validation using Postman.
• Develop cloud-based data ingestion solutions using Azure Data Lake Storage Gen2 (ADLS) as the landing layer and Azure Key Vault for secure credential management.
• Design metadata-driven ELT frameworks with incremental loading, audit logging, watermark processing, and automated data orchestration.
• Optimize Snowflake performance through warehouse sizing, query tuning, clustering strategies, Time Travel, Cloning, and warehouse management best practices.
• Collaborate with DevOps teams using Azure DevOps for source control, CI/CD deployment, release management, and Agile delivery.
• Design dimensional data models, build curated data marts, and support enterprise reporting and analytics requirements.
• Develop and maintain Power BI semantic models, datasets, dashboards, and reports; knowledge of DAX, Power Query, and data visualization best practices is preferred.
• Work closely with business stakeholders, solution architects, and cross-functional teams to deliver secure, scalable, and high-performance cloud data platform solutions.

Thanks,

Mounika P

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!

• Design, build, and maintain scalable ETL/ELT pipelines for batch and real-time data ingestion and transformation.
• Develop and optimize data lake and data warehouse architectures (e.g., Snowflake, BigQuery, Redshift).
• Work with cloud platforms GCP, Azure to manage data infrastructure.
• GCP as mandatory skills
• Collaborate with analytics and product teams to understand data needs and deliver solutions.
• Ensure data quality, reliability, security, and compliance across all data systems.
• Mentor junior data engineers and contribute to best practices and code reviews.
• Monitor and troubleshoot data pipeline performance and resolve data-related issues.
• Automate data validation, monitoring, and alerting processes.
• 8+ years of experience in data engineering or software engineering with a data focus.
• Proficient in SQL and at least one programming language (e.g., Python, Scala, Java).
• Experience with modern data warehousing tools (e.g., Snowflake, Redshift, BigQuery).
• Strong understanding of data modeling, data lakes, and ETL/ELT design.
• Hands-on experience with orchestration tools like Airflow, dbt, or similar.
• Solid experience with cloud data platforms (AWS/GCP/Azure).
• Familiarity with CI/CD pipelines, containerization (Docker/Kubernetes), and version control (Git).
• Experience working in a DevOps or DataOps environment.
• Knowledge of data governance, lineage, and cataloging tools (e.g., Collibra, Alation).
• Familiarity with streaming technologies (Kafka, Spark Streaming, Flink).
• Experience supporting machine learning workflows and data science initiatives.

Job description: Data Architect – Databricks / AWS

Job Summary

We are looking for an experienced Data Architect to define and drive the target data architecture for the Horizon MVP and its future evolution. The role will be responsible for designing a scalable, secure, governed cloud data platform covering ingestion, storage, processing, analytics, APIs, and downstream data consumption.

The architect will work closely with Data Engineering, Backend, DevOps, QA, and business stakeholders to establish architecture standards and ensure the platform is ready for advanced analytics, AI/ML, vector storage, and future LLM-based capabilities.

• Job Title: Data Architect
• Experience: 8+ Years
• Relevant Architecture Experience: 3+ Years in Data Architecture
• Location: Chennai / Pune
• Work Mode: Hybrid – 3 Days WFO
• Budget: Up to 24 LPA
• Payroll: Haparz
• Notice Period: Immediate Preferred

Key Responsibilities

• Define the target data architecture for the Horizon MVP and establish an architecture roadmap for future scalability.
• Design end-to-end architecture covering data ingestion, storage, processing, serving, reporting, APIs, and downstream applications.
• Establish canonical data models and schemas for travel signals, corridors, sources, evidence, scores, and generated insights.
• Define data normalization strategies for structured, semi-structured, and unstructured data from multiple external sources.
• Design and govern the Databricks platform architecture, including Unity Catalog, data schemas, access controls, and governance standards.
• Establish data-retention, lineage, data-quality, security, privacy, and compliance controls.
• Define secure integration patterns between Databricks, AWS PRODIGY, SharePoint, external APIs, and downstream applications.
• Design scalable data processing for 30-day signal windows, convergence/divergence scoring, corridor ranking, and spike detection.
• Define architecture patterns that support future vector storage, embeddings, LLM integration, and multi-year analytics.
• Design reliable batch and API-driven ingestion frameworks for structured and unstructured data.
• Review technical designs, identify architectural risks, and provide technical direction to engineering teams.
• Guide backend, data engineering, DevOps, and QA teams in implementing architecture standards.
• Ensure architecture decisions align with enterprise security, RBAC, PII handling, privacy, and operational requirements.
• Communicate architecture decisions, trade-offs, and technical recommendations effectively to technical and business stakeholders.
• 
  

What We’re Looking For

• 8+ years of experience in data engineering, data platforms, or data architecture, with at least 3+ years in a Data Architect capacity.
• Strong hands-on experience designing cloud-based data platforms, lakehouses, or analytical platforms.
• Advanced knowledge of Databricks, Apache Spark/PySpark, Delta Lake, and Unity Catalog.
• Strong understanding of AWS data services, IAM, networking, and secure cloud integration patterns.
• Strong expertise in data modelling, metadata management, data lineage, data quality, retention, and governance.
• Experience architecting batch and API-based ingestion pipelines for structured, semi-structured, and unstructured data.
• Understanding of AI/ML workloads, feature pipelines, vector databases, embeddings, and LLM integration patterns.
• Experience designing APIs and downstream data-serving architectures.
• Strong knowledge of PII protection, RBAC, data privacy, and enterprise security controls.
• Excellent architectural communication and stakeholder-management skills.

Strong Databricks Architect Profile with end-to-end Lakehouse ownership

2

Mandatory (Experience 1) – Must have 10+ years of software engineering experience with atleast 5+ years in Data Engineering with hands on exposure to Databricks and strong ownership of end-to-end data pipeline development.

3

Mandatory (Experience 2) – Must have atleast 5+ years of expertise across the Databricks ecosystem — Delta Lake, Delta Live Tables, Autoloader, Structured Streaming, Workflows, Unity Catalog

4

Mandatory (Tech skill 1) – Must have worked at architecture level, owning end-to-end design through deployment

5

Mandatory (Tech skill 2) – Must have strong experience with Python and SQL for data processing and Apache Spark for performance tuning & scalability

6

Mandatory (Tech skill 3) – Must have experience in large-scale data warehousing & advanced data modeling (3NF and dimensional) across batch and real-time systems

7

Mandatory (AI Exposure) – Must have at least a basic working understanding of how AI services or tools work

8

Mandatory (Communication) – Must have strong stakeholder management & requirement-gathering experience with US or UK clients

9

Mandatory (Company) – Must come from a B2B IT services or IT consulting background

10

Mandatory (Note) – CTC is inclusive of 5% variable

11

Preferred (Tech skill 1) – Azure Databricks or Azure data services experience (project runs on Azure DevOps)

12

Preferred (Tech skill 2) – MLflow or MLOps practices and AI use cases (RAG, AI/BI)

13

Preferred (Tech skill 3) – CI/CD, Databricks Asset Bundles (DABs) or equivalent packaging, Terraform or IaC, reusable deployment templates

14

Preferred (Integrations) – ServiceNow or enterprise system integrations

15

Preferred (Certifications) – Databricks (Data Engineer Associate or Professional, ML or GenAI tracks), Azure or AWS cloud certifications

10+ years of experience in Data Architecture, Data Engineering, or Data Platforms

• Strong expertise in IBM DB2 / On-Prem Relational Databases

• Strong expertise in PostgreSQL

• Hands-on experience with the Azure Data Ecosystem, including:

▪ Azure Data Factory (ADF)

▪ Azure Data Lake Storage Gen2 (ADLS Gen2)

▪ Azure Databricks / Synapse Analytics

▪ Azure Event Hub / Service Bus

▪ Azure Functions