Data Engineer ( Azure )

Strong Azure Databricks Engineer / Senior Data Engineer Profile

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Mandatory (Experience 1) – Must have minimum 8+ years of overall experience in Data Engineering, Data Development, or related data technology roles, with strong hands-on experience in enterprise data pipeline development.

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Mandatory (Experience 2) – Must have strong hands-on experience with Azure Databricks, including development and optimization of scalable data engineering workflows using Databricks and PySpark.

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Mandatory (Experience 3) – Must have strong hands-on proficiency in PySpark/Python and SQL, with proven experience developing complex data transformations, processing workflows, and performance-optimized queries.

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Mandatory (Experience 4) – Must have hands-on experience with Azure Data Factory (ADF) for designing, developing, and orchestrating end-to-end data pipelines and integrating data from multiple sources.

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Mandatory (Experience 5) – Must have strong experience working on the Azure Cloud platform and associated data services, with solid understanding of data warehousing, data modeling, pipeline architecture, and enterprise data solutions.

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Mandatory (Experience 6) – Must have hands-on experience implementing CI/CD using Azure DevOps, including deployment and release management across development, QA, and production environments.

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Mandatory (Experience 7) – Must have proven technical leadership experience, including code reviews, enforcing development best practices, mentoring developers, and providing technical guidance to a data engineering team.

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Mandatory (Notice Period) – Immediate joiners or candidates who can join within 15 days.

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Mandatory (Note) - The position is open across all Cognizant offices pan India. Candidates must be willing to attend the F2F interview at the nearest Cognizant office location.

Roles & Responsibilities

• Design, develop, and deliver scalable end-to-end data pipelines using Azure Data Factory, ensuring robust integration

of enterprise-wide data from diverse sources

• Build and optimize data engineering workflows using Databricks and PySpark

• Write efficient, high-performance SQL for data transformation and analysis

• Work with the Azure Cloud platform and associated services, applying strong understanding of data warehousing,

data models, and pipelines

• Provide technical leadership to a team of developers, including code reviews and enforcing best practices across the

development lifecycle

• Oversee CI/CD implementation using Azure DevOps, managing deployments across development, QA, and production

environments with proper change control processes

• Collaborate with cross-functional teams to translate business requirements into scalable data solutions

• Ensure data quality, reliability, and performance across all pipelines and platforms

Ideal Candidate

1Strong Azure Databricks Engineer / Senior Data Engineer Profile

2Mandatory (Experience 1) – Must have minimum 8+ years of overall experience in Data Engineering, Data Development, or related data technology roles, with strong hands-on experience in enterprise data pipeline development.

3Mandatory (Experience 2) – Must have strong hands-on experience with Azure Databricks, including development and optimization of scalable data engineering workflows using Databricks and PySpark.

4Mandatory (Experience 3) – Must have strong hands-on proficiency in PySpark/Python and SQL, with proven experience developing complex data transformations, processing workflows, and performance-optimized queries.

5Mandatory (Experience 4) – Must have hands-on experience with Azure Data Factory (ADF) for designing, developing, and orchestrating end-to-end data pipelines and integrating data from multiple sources.

6Mandatory (Experience 5) – Must have strong experience working on the Azure Cloud platform and associated data services, with solid understanding of data warehousing, data modeling, pipeline architecture, and enterprise data solutions.

7Mandatory (Experience 6) – Must have hands-on experience implementing CI/CD using Azure DevOps, including deployment and release management across development, QA, and production environments.

8Mandatory (Experience 7) – Must have proven technical leadership experience, including code reviews, enforcing development best practices, mentoring developers, and providing technical guidance to a data engineering team.

9Mandatory (Notice Period) – Immediate joiners or candidates who can join within 15 days.

10Mandatory (Note) - The position is open across all Cognizant offices pan India. Candidates must be willing to attend the F2F interview at the nearest Cognizant office location.

Job Title : Data Engineer – Databricks

Experience : 6+ Years

Location : Noida / Hyderabad / Chennai / Pune / Bengaluru (Hybrid)

Shift : IST (Normal Shift)

Job Summary :

We are seeking an experienced Data Engineer with strong expertise in Databricks, Snowflake, Python, and Spark to build and optimize scalable data pipelines and support AI/ML model deployments. The ideal candidate should have experience working with cloud-based data platforms and preferably possess exposure to the Healthcare domain.

Required Skills :

• Databricks (Preferred)
• Snowflake
• Python
• Apache Spark
• SQL
• Azure Cloud
• Kubernetes
• Apache Airflow
• GitHub & CI/CD Pipelines
• AI/ML Model Deployment
• Data Analytics

Preferred :

• Experience in the Healthcare domain.
• Strong understanding of scalable data engineering architectures and best practices.

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

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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.

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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

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

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

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

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

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

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

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

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

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

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

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Preferred (Integrations) – ServiceNow or enterprise system integrations

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

About the Role

We are looking for a Senior Data Engineer with strong hands-on expertise in Databricks, Python, PySpark, and SQL to build scalable, high-performance data engineering solutions. You’ll architect and develop large scale, high-performance data pipelines capable of handling massive real-time and batch data volumes across multiple business systems. Databricks is the core enterprise data and processing platform for this role. You will also use Apache Airflow for workflow orchestration and dbt for ELT transformations, and will contribute to designing reliable, secure, and governed data platforms that enable analytics, reporting, and AI-driven use cases.

Key Responsibilities

• Design and implement large-scale data pipelines using Python/PySpark, Databricks, and Microsoft Fabric.
• Develop and optimize data processing workloads in Databricks using PySpark and Spark SQL, with a strong focus on scalability, reliability, performance, and maintainability.
• Develop and maintain dbt models including layered architecture, incremental models, snapshots, macros, testing, and documentation.
• Design, develop, and maintain Apache Airflow DAGs for orchestrating reliable, scalable, and observable data pipelines.
• Design and implement data quality, observability, and governance frameworks, including automated testing, monitoring, lineage, access control, and data privacy standards.
• Partner with analytics, product, and business stakeholders to turn requirements into trustworthy datasets, and raise the engineering bar through design discussions, code reviews, and mentoring junior engineers.

Required Skills

• Strong expertise in Python for developing scalable, modular, and production-ready data engineering applications.
• Strong expertise in PySpark, including DataFrame API, Spark SQL, Structured Streaming, partitioning strategies, joins, caching, handling data skew, and Spark performance optimization.
• Strong hands-on experience with Databricks for data ingestion, transformation, processing, and optimization, including Delta Lake, Unity Catalog, Databricks Workflows, notebooks, jobs, and Databricks-native data engineering capabilities.
• Strong experience in Databricks/Spark performance tuning, including query and job optimization, partitioning, file sizing, caching, join optimization, handling data skew, and efficient use of compute resources.
• Hands-on experience with Delta Lake, including transactional data processing, schema management, incremental data processing, and reliable batch and streaming data pipelines.
• Hands-on experience in developing dbt projects using layered architecture, incremental models, snapshots, macros/Jinja, testing, documentation, and deployment best practices.
• Expertise in advanced SQL and data modelling — dimensional modeling, slowly changing dimensions, schema evolution, and query optimization.
• Hands-on experience in developing and managing Apache Airflow DAGs, scheduling workflows, dependency management, retries, backfills, and operational monitoring.
• Hands-on experience with at least one major cloud platform (AWS, Azure or GCP).
• Strong problem-solving skills and the ability to work independently with business and analytics stakeholders.

Nice to Have

• Hands-on exposure to Microsoft Fabric for data integration and analytics.
• Experience using AI coding assistants (e.g. Claude Code, GitHub Copilot) as part of a development workflow.
• Familiarity with modern DevOps practices, including CI/CD pipelines, Infrastructure as Code (IaC), and containerization (Docker/Kubernetes).
• Domain expertise in financial services.

Job Description

• Design and Implement Data Solutions: Lead the design, development, and implementation of scalable and secure Azure-

based data platforms, ensuring integration with various data sources and business systems. Deliver at least 2 major

projects every year with a focus on data engineering best practices.

• Optimize Data Pipelines: Build and optimize end-to-end data pipelines using Azure Data Factory, Azure Databricks, and

Azure Synapse, with an emphasis on automating data workflows. Achieve a 20% reduction in pipeline execution times

within the first 6 months.

• Cloud Infrastructure Management: Manage and maintain the Azure data environment, ensuring high availability, disaster

recovery, and cost optimization. Track and improve system uptime to exceed 99.9% reliability.

• Collaborate with Cross-Functional Teams: Partner with data scientists, data analysts, and business stakeholders to translate

business requirements into efficient data solutions. Facilitate at least 3 collaborative sessions per quarter to address key

business use cases.

• Ensure Data Security & Compliance: Implement data security measures, ensuring compliance with industry regulations

(GDPR, HIPAA, etc.) and company policies. Achieve and maintain full compliance in all data environments within the first

quarter of onboarding.

• Continuous Learning & Knowledge Sharing: Stay up-to-date with emerging Azure technologies, and mentor junior

engineers to promote knowledge sharing. Complete 1 Azure certification annually and conduct at least 2 internal

knowledge-sharing sessions per year.

• Sound knowledge of data governance practices, data quality management, and data security principles.

• Play a pivotal role in shaping our organization's data-driven journey, driving innovation through data analytics and insights.

• Optimize data storage, processing and retrieval mechanisms for performance, cost, and scalability using data storage

services (such as Azure Data Lake Storage, Azure SQL Database, etc.), data processing services (such as Azure Data Bricks,

Azure Synapse, etc.) and data visualization (PowerBI, Qlik, etc.) & integration services (Data API builder, logic apps, etc.)

• Monitor and troubleshoot data platform performance, identify and resolve issues, and provide recommendations for

continuous improvement.

• Collaborate with DevOps teams to automate deployment, configuration, and monitoring processes using Azure DevOps,

PowerShell, or other relevant tools.

• Stay up to date with the latest trends and advancements in cloud data services and provide recommendations on adopting

new technologies or features to enhance the data platform.

• Document technical designs, procedures, and guidelines for data platform engineering and operations

Knowledge, Skills & Experience

Job Experience • Bachelor's degree in Computer Science, Engineering, or a related field. Advanced

degree preferred.

• Proven 6-10 years experience in playing platform engineer or admin role

• Experience with big data technologies such as Apache Spark, Hadoop, or similar

frameworks.

• Solid understanding of cloud computing concepts and experience with cloud

infrastructure management and provisioning.

• Solid understanding of network security concepts and technologies (such as

firewalls, VPNs, intrusion detection/prevention systems, etc.) and data security

concepts and technologies (such as access controls, encryption, observability,

privacy laws/regulations, etc.)

• Experience in a Retail setup is preferred.

Required Skills The position will require someone with the following:

• Strategic Planning

Public

• Communication and Collaboration

• Problem Solving Skills A/B testing & experimentation

• SQL, BI tools, and storytelling with data

Job Title : Senior Data Engineer – Databricks

Experience : 14 to 20 Years

Location : HSR Layout, Bangalore

Work Mode : Hybrid – 3 Days WFO

Shift : 11:30 AM – 07:30 PM IST

Positions : 2

Notice Period : Immediate Joiners Only

Interview : 1 Technical Round + 2 Client Rounds

Role Overview :

We are looking for a Senior Data Engineer to build and lead enterprise-scale data platforms for a Switzerland-based commodity client.

The role requires a strong hands-on Data Engineering professional with expertise in Databricks, PySpark, Python, SQL, and AWS, along with technical leadership and stakeholder management experience.

Must-Have Skills :

• 14 to 20 years of Data Engineering experience
• Databricks & Apache Spark / PySpark
• Python & SQL
• AWS Cloud
• Lakehouse Architecture
• ETL / ELT & Distributed Data Processing
• Batch & Streaming Pipelines
• Data Pipeline Optimization & Data Modeling
• CDC & Incremental Processing
• Git, CI/CD & Testing
• Data Quality, Monitoring & Observability
• Technical Leadership & Stakeholder Management

Key Responsibilities :

• Design and build scalable data pipelines using Databricks, PySpark, Python, SQL, and AWS.
• Own data products from design through production.
• Develop batch / streaming pipelines and reusable ETL / ELT frameworks.
• Optimize pipelines for performance, scalability, reliability, and cost.
• Design scalable data architectures and data models.
• Implement data quality, monitoring, lineage, and CI/CD practices.
• Lead technical discussions and mentor engineering teams.
• Collaborate with business stakeholders, architects, product owners, and engineering teams.
• Remain hands-on while providing technical leadership.

Ideal Candidate :

A 14 to 20 years experienced, hands-on Data Engineering leader with strong Databricks + PySpark + AWS expertise, excellent communication, stakeholder management, and experience delivering enterprise-scale data platforms.

🔴 Super Urgent : Only Bangalore-based immediate joiners.

Hiring : Senior Databricks AI Architect

Exp : 15 - 18 yrs

Work Location : Pune WFO

Skills :

10 +years of experience in Data Engineering, Data Architecture, Analytics, or Software Engineering.

Minimum 5 years of hands-on experience with Databricks (Mandatory).

Strong expertise in designing and implementing enterprise-scale data platforms on Databricks.

Hands-on experience with AI-powered engineering tools such as Databricks Genie, Cursor, GitHub Copilot, or similar AI platforms.

Strong proficiency in Python, SQL, Spark, Delta Lake, and Databricks notebooks.

Excellent communication, stakeholder management

Dear Candidate,

Greeting from NAM Info Pvt Ltd.

We have a role for Data Engineer position with NAM Info.

This role will be permanent with NAM info and deploy to client

location NEW DELHI~CHENNAI~HYDERABAD~PUNE~KOLKATA.

Work Mode: WORK FROM OFFICE

A decent hike can be provided based on current CTC

Interview Mode: Virtual

Role Descriptions:

Exp Range: 7 - 10 years

City Locations: NEW DELHI~CHENNAI~HYDERABAD~PUNE~KOLKATA

Key Responsibilities*

Role: Data Engineer

Location: ~NEW DELHI~CHENNAI~HYDERABAD~PUNE~KOLKATA

Skills: Digital: Databricks, Azure Data Factory

Experience Required: 8-10

Descriptions:

Good information and sound knowledge in Azure Synapse Analytics Azure Data Factory (ADF)Big Data technologies and data processing frameworks Azure Data Warehouse and associated Azure data platform services Data integration| data modelling| and performance optimization

Desire candidate

• Candidate should have valid PF.

Regards,

NAM Info 

Azure Data Factory and Azure Databricks, processing 5 million+ records/week from 4+ source systems into a governed Lakehouse.