Data Engineer

8-12 years experience on Data Engineering role working with Databricks & Azure Cloud technologies.

• Bachelor’s degree in computer science, Information Technology, or related field.

• Strong proficiency in PySpark, Python, SQL.

• Strong experience in data modeling, ETL/ELT pipeline development, and automation • Hands-on experience with performance tuning of data pipelines and workflow

• Proficient in working on Azure cloud components Azure Data Factory, Azure DataBricks, Azure Data Lake etc.

• Experience with data modeling, ETL processes, Delta Lake and data warehousing.

• Experience on Delta Live Tables, Autoloader & Unity Catalog.

Preferred - Knowledge of the insurance industry and its data requirements.

• Strong analytical skills with the ability to collect, organize, analyze, and disseminate significant amounts of information with attention to detail and accuracy.

• Excellent communication and problem-solving skills to work effectively with diverse teams

• Excellent problem-solving skills and ability to work under tight deadlines. 

Solution Architect – AZURE Data Engineering

Job Overview

We are looking for an experienced Solution Architect – Data Engineering with strong expertise in designing data solutions and hands-on experience with Azure, Synapse, PySpark, Data Warehousing, and Data Lakes. The ideal candidate should have strong architectural and data engineering knowledge.

Key Responsibilities

• Design and implement scalable data architecture and solutions.
• Develop and manage Data Warehouse and Data Lake architectures.
• Design data platforms using Medallion Architecture.
• Lead Data Engineering and ETL activities.
• Work with Azure Synapse Analytics for data processing and analytics.
• Develop data solutions using PySpark / Apache Spark.
• Define and implement data validation and data quality processes.
• Collaborate with business, data, and technology teams to deliver effective data solutions.

Required Skills

• Strong experience in Solution Architecture / Data Architecture.
• Strong knowledge of Data Warehouse and Data Lake architecture.
• Good understanding of Medallion Architecture.
• Strong experience in Data Engineering and ETL.
• Hands-on experience with Microsoft Azure and Azure Synapse Analytics.
• Strong knowledge of PySpark / Apache Spark.
• Experience with Data Validation and Data Quality.
• Good communication and stakeholder management skills.
• 
  

Experience

8+ Years

Description

We are looking for Senior Data Engineers to join our Data Platform team and build scalable, high-performance data platforms that power data processing, analytics, and downstream applications.

The ideal candidate will have strong experience in distributed data processing, ETL pipelines, and Big Data technologies, with hands-on expertise in Apache Spark and Python Scala.

You will be responsible for designing, developing, and optimizing large-scale data pipelines while collaborating closely with cross-functional engineering teams to build reliable, production-grade data solutions.

Key Responsibilities

• Design, develop, and maintain scalable ETL and data processing pipelines for large-scale datasets.
• Build and optimize distributed data applications using Apache Spark and Python Scala.
• Develop reliable, high-performance data pipelines for batch and streaming workloads.
• Design and manage data workflows using Apache Airflow.
• Build and operate data workloads on AWS, with strong usage of Amazon S3 for large-scale data storage.
• Work with large datasets to ensure data quality, consistency, reliability, and performance.
• Collaborate with engineering, product, analytics, and other platform teams to deliver robust data solutions.
• Optimize data workflows for scalability, reliability, performance, and cost efficiency.
• Troubleshoot production issues, identify bottlenecks, and continuously improve platform performance.

Requirements

Candidates who demonstrate:

• 5+ years of experience in Data Engineering, Big Data Engineering, or a similar role.
• Strong hands-on experience with Apache Spark and Scala.
• Experience designing, building, and maintaining large-scale ETL pipelines.
• Strong hands-on experience with AWS, particularly Amazon S3.
• Hands-on experience with Apache Airflow for workflow orchestration and scheduling.
• Strong SQL skills and a solid understanding of distributed data processing concepts.
• Experience working with batch and/or streaming data pipelines.
• Excellent debugging, problem-solving, and performance optimization skills.
• Strong communication and collaboration skills.

Good to Have

• Experience with Databricks and the broader Databricks data platform.
• Familiarity with streaming technologies such as Apache Kafka.
• Experience working on large-scale data platforms handling high-volume data workloads.
• Exposure to additional AWS data services and cloud-native data architectures.

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.

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.

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.

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!

Job Description:

We are seeking a skilled Senior Data Engineer with expertise in Databricks to join our dynamic data team. The ideal candidate will design, build, and maintain scalable data pipelines and architectures to support our organization's data-driven initiatives and leverage Databricks to process large-scale datasets, optimize data workflows, enable advanced analytics and machine learning, and integrate Power BI for data visualization and reporting.

Key Responsibilities

·        5-8 years of professional work experience in a relevant field

·        Proficient in Microsoft Fabric platform, Azure Databricks, ADF, Delta Lake, SQL Data Warehouse, Unity Catalog.

·        Good Experience on Microsoft Dynamics 365

·        Experience/ prior knowledge on semi structure data and Structured Streaming, Azure synapse, data lake, data warehouse.

·        Proficient in creating Azure Data Factory pipelines for ETL/ELT processing; copy activity, custom Azure development etc.

·        Good knowledge of SQL and Python for data manipulation, transformation, and analysis

·        Understand business requirements to set functional specifications for reporting applications

• Data Pipeline Development: Design, develop, and maintain robust, scalable data pipelines using Databricks, Apache Spark, and other cloud-based technologies.
• Data Integration: Ingest, transform, and integrate data from diverse sources, including APIs, databases, streaming platforms, and third-party systems, into Databricks for analytics and reporting.
• Power BI Integration: Develop and optimize data models and datasets in Databricks for use in Power BI, ensuring efficient data connections and high-quality visualizations.
• Performance Optimization: Optimize data workflows, API calls, and queries on Databricks and Power BI for performance, cost-efficiency, and scalability.
• Data Modeling: Build and maintain data models to support business requirements, ensuring data quality, consistency, and accessibility for analytics and reporting.
• Cloud Integration: Implement data solutions on cloud platforms (e.g., AWS, Azure, GCP) integrated with Databricks, Power BI, and API ecosystems.
• Security & Compliance: Implement data governance, security, and compliance best practices within Databricks, Power BI, and API environments.
• Technical Skills:
• Proficiency in Databricks, including Delta Lake, Spark SQL.
• Strong programming skills in Python, Scala, or Java.
• Experience with Apache Spark for big data processing.
• Knowledge of SQL for querying and transforming data.
• Proficiency in Power BI for creating data models, DAX queries, and interactive dashboards.

Preferred Qualifications

• Databricks certification (e.g., Databricks Certified Data Engineer Associate/Professional).

Experience with real-time data processing and streaming

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.

Description

We are looking for Senior Data Engineers to join our AdTech team and build scalable, high-performance data platforms that power advertising insights and analytics. The ideal candidate will have strong experience in distributed data processing, ETL pipelines, and Big Data technologies, with hands-on expertise in Spark and Scala.

You will work on designing, developing, and optimizing large-scale data pipelines while collaborating closely with cross-functional engineering teams to build reliable, production-grade data solutions.

Key Responsibilities

• Design, develop, and maintain scalable ETL pipelines for large-scale data processing.
• Build and optimize distributed data applications using Spark and Scala.
• Develop reliable, high-performance data pipelines for batch and streaming workloads.
• Work with large datasets to ensure data quality, consistency, and performance.
• Collaborate with engineering, product, and analytics teams to deliver robust data solutions.
• Optimize data workflows for scalability, reliability, and cost efficiency.
• Deploy and manage data workloads in cloud and containerized environments.
• Troubleshoot production issues and continuously improve platform performance.

Requirements

Candidates who demonstrate:

• 5+ years of experience in Data Engineering or Big Data Engineering.
• Strong hands-on experience with Apache Spark and Scala.
• Experience building and maintaining ETL pipelines.
• Familiarity with Google Cloud Storage (GCS).
• Experience with Kubernetes (K8s).
• Strong SQL skills and understanding of distributed data processing.
• Excellent debugging, problem-solving, and performance optimization skills.
• Strong communication and collaboration skills.

Good to Have

• Experience with AWS and cloud-native data services.
• Familiarity with streaming technologies such as Kafka.
• Experience working on large-scale data platforms or AdTech systems.
• Exposure to orchestration tools such as Airflow.

Benefits

• Best-in-class salary: We hire strong talent and compensate accordingly.
• Proximity Talks: Meet and learn from designers, engineers, product leaders, and AI practitioners.
• Continuous learning: Work with a world-class team and stay close to the latest in AI, engineering, and product development.
• High-impact work: Build AI-first systems and products used at scale by global clients.

About Us

Proximity is the trusted technology, design, and consulting partner for some of the biggest Sports, Media, and Entertainment companies in the world. We’re headquartered in San Francisco and have offices in Palo Alto, Dubai, Mumbai, and Bangalore.

Since 2019, Proximity has built high-impact, scalable products used by millions of users every day. Today, we are a global team of engineers, designers, product managers, and experts solving complex problems and building cutting-edge technology at scale.