Hiring for Data Engineer - Ecommerce

Strong Data Engineer (AWS) Profile

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Mandatory (Experience 1) – Must have minimum 6+ years of experience in Data Engineering, with strong experience designing, developing, and maintaining scalable data pipelines and ETL processes.

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Mandatory (Experience 2) – Must have 3+ years of hands-on experience with AWS data engineering services, particularly S3, AWS Glue, EMR, Lambda, and Redshift.

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Mandatory (Experience 3) – Must have strong programming experience in Python, Java, or Scala, with hands-on development of data processing and pipeline solutions.

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Mandatory (Experience 4) – Must have strong expertise in SQL, data warehousing, and database technologies, including experience with both SQL and NoSQL databases.

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Mandatory (Experience 5) – Must have hands-on experience working with Big Data technologies and distributed data processing, including designing and optimizing large-scale data pipelines.

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Mandatory (Experience 6) – Must have experience with data pipeline troubleshooting, monitoring, data quality, governance, and security best practices.

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Mandatory (Education) – B.Tech / B.E./ M.Tech

Strong Data Engineer (AWS) Profile

2

Mandatory (Experience 1) – Must have minimum 6+ years of experience in Data Engineering, with strong experience designing, developing, and maintaining scalable data pipelines and ETL processes.

3

Mandatory (Experience 2) – Must have 3+ years of hands-on experience with AWS data engineering services, particularly S3, AWS Glue, EMR, Lambda, and Redshift.

4

Mandatory (Experience 3) – Must have strong programming experience in Python, Java, or Scala, with hands-on development of data processing and pipeline solutions.

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Mandatory (Experience 4) – Must have strong expertise in SQL, data warehousing, and database technologies, including experience with both SQL and NoSQL databases.

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Mandatory (Experience 5) – Must have hands-on experience working with Big Data technologies and distributed data processing, including designing and optimizing large-scale data pipelines.

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Mandatory (Experience 6) – Must have experience with data pipeline troubleshooting, monitoring, data quality, governance, and security best practices.

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Mandatory (Education) – B.Tech / B.E./ M.Tech

Job Title : AWS Data Engineer

Experience : 4+ Years

Location : Bengaluru (HSR – Hybrid, 3 Days WFO)

Notice Period : Immediate Joiner

💡 Role Overview :

We are looking for a skilled AWS Data Engineer to design, build, and scale modern data platforms. The role involves working with AWS-native services, Python, Spark, and DBT to deliver secure, scalable, and high-performance data solutions in an Agile environment.

🔥 Mandatory Skills :

Python, SQL, Spark, AWS (S3, Glue, EMR, Redshift, Athena, Lambda), DBT, ETL/ELT pipeline development, Airflow/Step Functions, Data Lake (Parquet/ORC/Iceberg), Terraform & CI/CD, Data Governance & Security

🚀 Key Responsibilities :

• Design, build, and optimize ETL/ELT pipelines using Python, DBT, and AWS services
• Develop and manage scalable data lakes on S3 using formats like Parquet, ORC, and Iceberg
• Build end-to-end data solutions using Glue, EMR, Lambda, Redshift, and Athena
• Implement data governance, security, and metadata management using Glue Data Catalog, Lake Formation, IAM, and KMS
• Orchestrate workflows using Airflow, Step Functions, or AWS-native tools
• Ensure reliability and automation via CloudWatch, CloudTrail, CodePipeline, and Terraform
• Collaborate with data analysts and data scientists to deliver actionable insights
• Work in an Agile environment to deliver high-quality data solutions

✅ Mandatory Skills :

• Strong Python (including AWS SDKs), SQL, Spark
• Hands-on experience with AWS data stack (S3, Glue, EMR, Redshift, Athena, Lambda)
• Experience with DBT and ETL/ELT pipeline development
• Workflow orchestration using Airflow / Step Functions
• Knowledge of data lake formats (Parquet, ORC, Iceberg)
• Exposure to DevOps practices (Terraform, CI/CD)
• Strong understanding of data governance and security best practices
• Minimum 4–7 years in Data Engineering (3+ years on AWS)

➕ Good to Have :

• Understanding of Data Mesh architecture
• Experience with platforms like Data.World
• Exposure to Hadoop / HDFS ecosystems

🤝 What We’re Looking For :

• Strong problem-solving and analytical skills
• Ability to work in a collaborative, cross-functional environment
• Good communication and stakeholder management skills
• Self-driven and adaptable to fast-paced environments

📝 Interview Process :

1. Online Assessment
2. Technical Interview
3. Fitment Round
4. Client Round

🚀 Job Title : Data Engineer – AWS, PySpark & Airflow

Experience : 5+ Years

Industry : Health Insurance / BFSI

Location : Sector 43, Gurugram, Haryana

Work Mode : Work From Office (5 Days a Week)

Employment Type : Full-Time

Role Overview :

We are looking for an experienced Data Engineer with strong hands-on expertise in AWS Data Engineering, Data Lake Architecture, Python, SQL, PySpark, and Apache Airflow.

The candidate will be responsible for designing, developing, and maintaining scalable, reliable, and high-performance data pipelines using AWS cloud technologies. The role involves building end-to-end ETL / ELT workflows, implementing data transformation and processing solutions, optimizing pipeline performance, and ensuring the reliability of production data systems.

Mandatory Skills :

AWS Data Engineering | Data Lake Architecture | Amazon S3 | AWS Glue | Amazon EMR | Athena | Redshift | Python | SQL | PySpark | Apache Spark | Apache Airflow | ETL/ELT | PostgreSQL | Data Modelling | Pipeline Optimization | Production Support | Root Cause Analysis

Key Responsibilities :

• Design, develop, and maintain end-to-end data pipelines for data ingestion, staging, transformation, curation, and consumption.
• Build and manage AWS-based data lake solutions using Amazon S3, AWS Glue, Amazon EMR, Athena, and Redshift.
• Develop, maintain, and optimize ETL / ELT pipelines using Python, SQL, Apache Spark, and PySpark.
• Implement and manage workflow orchestration using Apache Airflow, including scheduling, dependency management, retries, monitoring, and failure handling.
• Integrate data from multiple structured and semi-structured sources.
• Design and maintain data models, dimensional models, and analytical data marts.
• Work with relational databases, including PostgreSQL and Amazon RDS.
• Optimize Spark jobs, SQL queries, data transformations, and cloud resource utilization.
• Implement data quality checks, validation rules, and error-handling mechanisms.
• Monitor production data pipelines, troubleshoot failures, and perform root cause analysis (RCA).
• Ensure pipeline reliability, scalability, performance, and compliance with service-level agreements (SLAs).
• Follow data security, access management, governance, and sensitive-data protection best practices.
• Collaborate with business, analytics, and application teams to deliver effective data solutions.
• Maintain technical documentation and follow established data engineering standards.

Required Technical Skills :

• AWS Data Engineering : Strong hands-on experience with AWS-based data engineering and data lake architecture.
• AWS Services : Amazon S3, AWS Glue, Glue Data Catalog, Amazon EMR, Athena, Redshift, AWS Lambda, and IAM.
• Programming : Strong proficiency in Python and SQL.
• Big Data Processing : Hands-on experience with Apache Spark and PySpark.
• Workflow Orchestration : Experience developing and managing workflows using Apache Airflow.
• ETL/ELT Development : Experience building scalable data ingestion, transformation, and processing pipelines.
• Database Technologies : Good understanding of relational databases and PostgreSQL concepts.
• Data Modelling : Experience with data modelling, data transformation, and analytical data structures.
• Production Support : Ability to monitor pipelines, troubleshoot failures, perform RCA, and resolve production issues.
• Performance Optimization : Experience optimizing SQL queries, Spark jobs, and data processing workflows.

Good-to-Have Skills :

• Experience with AWS Database Migration Service (DMS) and Amazon RDS.
• Familiarity with dbt for data transformation and modelling.
• Knowledge of Delta Lake, Apache Iceberg, or Apache Hudi.
• Exposure to Apache Kafka and real-time data streaming.
• Familiarity with GraphQL, Hasura, or similar data-serving technologies.
• Understanding of data governance and data quality frameworks.
• Experience in the BFSI, healthcare, or health insurance domain.

Candidate Requirements :

• 5+ years of relevant experience in Data Engineering.
• Proven hands-on experience building and maintaining production-grade data pipelines on AWS.
• Strong understanding of data lake architecture and large-scale data processing.
• Practical experience with Apache Airflow and Spark / PySpark-based transformations.
• Strong programming, analytical, debugging, and problem-solving skills.
• Experience handling production incidents, pipeline failures, and performance bottlenecks.
• Ability to take ownership of deliverables and work effectively with cross-functional teams.
• Good communication, collaboration, and technical documentation skills.
• Willingness to work from the Gurugram office five days a week.

Ideal Candidate :

The ideal candidate is a hands-on Data Engineer who can independently build, optimize, and maintain AWS-based data pipelines, work with large datasets using PySpark, orchestrate workflows through Airflow, and resolve production issues in a business-critical environment.

Job Summary

Role Overview

We are looking for an experienced Data Engineer with strong expertise in Python, ETL, Advanced SQL, CI/CD, DevOps, and Data Analytics. The ideal candidate should have hands-on experience designing and developing scalable data pipelines, transforming large datasets, and supporting data-driven applications.

Experience with Google Cloud Platform (GCP) will be an added advantage.

Key Responsibilities

• Design, develop, and maintain scalable ETL/ELT data pipelines using Python and SQL.
• Develop complex and optimized SQL queries, stored procedures, and data transformations.
• Build and maintain reliable data integration workflows across multiple data sources.
• Perform data cleansing, validation, transformation, and quality checks.
• Analyze data and provide insights to support business and technical requirements.
• Implement and maintain CI/CD pipelines for data engineering applications.
• Work with DevOps practices and tools to automate deployments, monitoring, and infrastructure processes.
• Troubleshoot data pipeline failures, performance issues, and production incidents.
• Optimize data processing workflows for performance, scalability, and reliability.
• Collaborate with Data Analysts, Data Scientists, Developers, and other stakeholders.
• Follow best practices for version control, testing, documentation, and deployment.
• Contribute to cloud-based data engineering initiatives, preferably on GCP.

Required Skills

• 5–7 years of hands-on experience in Data Engineering.
• Strong programming skills in Python.
• Strong expertise in Advanced SQL and database concepts.
• Hands-on experience with ETL/ELT processes and data pipelines.
• Good understanding of Data Warehousing and Data Modeling concepts.
• Experience with CI/CD practices and tools.
• Strong understanding of DevOps principles, automation, and deployment processes.
• Strong data analytics and problem-solving skills.
• Experience working with large datasets and performance optimization.
• Good understanding of Git/version control and software development best practices.

Good to Have

• Hands-on experience with Google Cloud Platform (GCP).
• Exposure to GCP data services such as BigQuery, Cloud Storage, Dataflow, Composer, or Pub/Sub.
• Experience with containerization/orchestration technologies such as Docker/Kubernetes.
• Experience with workflow orchestration tools such as Airflow.
• Knowledge of cloud-based data architecture and distributed data processing.

Preferred Candidate Profile

• Strong analytical and problem-solving abilities.
• Good communication and stakeholder management skills.
• Ability to work independently as well as in a collaborative team environment.
• Strong ownership of data pipelines and production systems.
• Candidates who can join at short notice are preferred.

Mandatory Skills

 Data Engineer, Python , ETL, GCP, Advanced SQL, Strong Data Analytics skills, CICD, Devops

Job Title : Data / AI / GenAI – Engineering & Analytics

Experience : 4+ Years

Location : Noida / Gurugram / Ahmedabad

Employment Type : Full-time | On-site at client location

Working Days : 5 days per week

Working Hours : Standard business hours

Position Overview : We are seeking professionals to deliver modern data engineering, business intelligence, cloud analytics and AI-powered applications, according to their specialization.

Mandatory Skills :

Python, SQL, ETL/ELT, Data Engineering, Power BI, Azure Data Factory, Databricks, PySpark, Data Modeling, GenAI/LLMs, RAG, Vector Databases, AI Agents (as per role specialization).

Key Roles / Specializations :

1. Power BI Developers : Dashboards, DAX, Power Query, reporting and data modeling
2. Azure Data Engineers : Azure Data Factory, Data Lake, Azure SQL and cloud pipelines
3. Databricks Engineers : Apache Spark, PySpark, Delta Lake and scalable processing
4. Data Engineers : ETL/ELT, SQL, Python, data warehousing and quality
5. GenAI / RAG Engineers : LLMs, prompts, embeddings, vector databases and retrieval pipelines
6. Agentic AI Engineers : AI agents, tool calling, orchestration and multi-agent workflows

Key Responsibilities :

• Design, develop and maintain data pipelines, analytics solutions or AI applications.
• Build and optimize data integration, transformation and processing workflows.
• Develop dashboards and analytical models for business decision-making.
• Use cloud data platforms and modern engineering frameworks.
• Develop and evaluate LLM applications, RAG solutions or AI agents as applicable.
• Ensure data quality, security, performance and reliability.
• Collaborate with engineering teams and stakeholders on testing, deployment and documentation.

Required Skills & Qualifications :

• Bachelor’s degree in Computer Science, IT, Data Science, Engineering or related discipline.
• 4–5 years of relevant experience in data, analytics, AI or software engineering.
• Hands-on expertise in at least one listed specialization; not all technologies are required.
• Relevant Python, SQL, DAX or other technical skills depending on the position.
• Understanding of cloud platforms, APIs, integration and software practices.

Preferred Qualifications :

• Azure, Databricks or Microsoft Fabric experience.
• For GenAI roles : Azure OpenAI, OpenAI APIs, LangChain, LlamaIndex or LangGraph.
• For relevant roles : vector databases, CI/CD, Docker, MLOps or LLMOps.

Note : Candidates are expected to have expertise in their relevant specialization, not in every listed technology or role.

Job Description

We are looking for an experienced Data Engineer with strong expertise in Python, ETL, SQL, CI/CD, and DevOps to design, develop, and maintain scalable data pipelines and data processing solutions.

Key Responsibilities

• Design, develop, and maintain scalable ETL/ELT data pipelines.
• Develop data processing solutions using Python.
• Write complex and optimized SQL queries, stored procedures, and data transformations.
• Build and maintain data ingestion and integration workflows.
• Implement data quality, validation, monitoring, and error-handling processes.
• Develop and maintain CI/CD pipelines for data engineering applications.
• Work with DevOps tools and practices for automated build, deployment, and infrastructure management.
• Collaborate with data analysts, data scientists, software engineers, and business teams.
• Optimize data pipelines for performance, reliability, and scalability.
• Troubleshoot production data issues and ensure timely resolution.
• Follow best practices for version control, code quality, testing, and deployment.

Mandatory Skills

• Python
• ETL
• SQL
• CI/CD
• DevOps
• Git / Version Control
• Strong problem-solving and debugging skills

Job Title : Senior AWS Data Engineer – Data Lake Platform

Experience : 5+ Years

Location: Gurugram Sector 43, Haryana

Work Mode : Work From Office (5 Days a Week – Monday to Friday)

Open Positions : 8+

About the Role :

We are looking for a skilled Senior AWS Data Engineer with strong hands-on experience in building, managing, and optimizing enterprise-scale data lake platforms on AWS.

The ideal candidate will have expertise in AWS data services, PySpark, Apache Airflow, and large-scale data pipeline development. The role involves designing end-to-end data ingestion and processing workflows, maintaining data quality, optimizing platform performance, and supporting analytics and downstream data consumption requirements.

Mandatory skills :

AWS Data Lake, Amazon S3, EMR, Glue, Athena, Redshift, DMS, Lambda, RDS, IAM, PySpark, Apache Spark, Apache Airflow, Python, Advanced SQL, Data Modeling, Production Data Pipelines, Hasura/GraphQL, PostgreSQL, and Production Support.

Key Responsibilities :

• Design, develop, and maintain scalable AWS-based data lake platforms and end-to-end data pipelines.
• Build data ingestion, staging, transformation, curated, and consumption layers using AWS services.
• Develop and optimize data processing pipelines using PySpark, Apache Spark, and AWS EMR.
• Implement workflow orchestration and scheduling using Apache Airflow.
• Work with Amazon S3, AWS Glue, Glue Data Catalog, Amazon Athena, and Amazon Redshift for data storage, cataloging, processing, and analytics.
• Develop efficient SQL queries and implement data modeling strategies for enterprise reporting and analytics.
• Build and support data-serving solutions using Amazon RDS, AWS Lambda, API Gateway, and PostgreSQL-based platforms.
• Monitor pipeline performance, troubleshoot production issues, perform root-cause analysis, and ensure data pipeline SLAs.
• Implement data security, access controls, PII protection, and governance best practices across the platform.
• Optimize data processing performance, query execution, storage utilization, and AWS infrastructure costs.
• Collaborate with application, analytics, and business teams to deliver reliable and scalable data solutions.

Mandatory Skills :

• AWS Data Services : Amazon S3, EMR, Glue, Athena, Redshift, DMS, Lambda, RDS, and IAM.
• Data Engineering : Strong hands-on experience building and maintaining enterprise data lake platforms.
• Programming : Python, PySpark, and Apache Spark.
• Orchestration : Production experience with Apache Airflow.
• Database & SQL : Advanced SQL, data modeling, and query optimization.
• Data Architecture : Experience with data ingestion, staging, transformation, curated layers, and consumption architecture.
• Production Support : Pipeline monitoring, troubleshooting, root-cause analysis, and SLA management.
• API / Data Serving : Experience with Hasura or a similar GraphQL-based data-serving layer integrated with PostgreSQL.
• Platform Ownership : Ability to independently manage data pipelines, improve platform reliability, and collaborate with cross-functional teams.

Good-to-Have Skills :

• Experience with dbt for data transformation and modeling.
• Knowledge of data lakehouse architectures and table formats such as Apache Iceberg, Delta Lake, or Apache Hudi.
• Experience with Amazon Redshift workload optimization, distribution keys, sort keys, and Spectrum.
• Knowledge of Kafka and real-time or streaming data ingestion.
• Experience with PostgreSQL or Amazon Aurora as a data-serving layer.
• Familiarity with data governance, compliance, and sensitive-data management.
• Experience in the BFSI or health insurance domain.

Ideal Candidate Profile :

• 5+ years of hands-on experience in AWS Data Engineering.
• Strong experience building and supporting production-grade AWS data lake platforms.
• Practical expertise in PySpark, Apache Airflow, SQL, and AWS data services.
• Ability to troubleshoot complex data pipeline issues and optimize performance.
• Strong understanding of data architecture, data modeling, and production support practices.
• Candidates with experience in enterprise data platforms and insurance or BFSI environments will be preferred.

Job Title : Data Platform Engineer (SDE 2 / SDE 3)

Experience : 3 to 8 Years (SDE2 : 3 to 5 Years | SDE3 : 5.5 to 8 Years)

Location : Remote (Contract) → Pune (Post Conversion)

Employment Type : Contract-to-Hire (3 Months)

Mandatory Skills :

Java / Python / Go, SQL, Data Engineering, ETL / ELT, Apache Airflow, Apache Spark / Flink, Kafka, AWS/GCP/Azure, Distributed Systems, API Development, Docker, Kubernetes, CI/CD, System Design

Role Overview :

We are looking for a Data Platform Engineer with strong backend engineering expertise to build scalable, high-performance data platforms and distributed systems. The ideal candidate should have hands-on experience in developing production-grade backend applications along with designing and maintaining modern data pipelines.

Key Responsibilities :

• Build and maintain scalable ETL/ELT and data processing pipelines.
• Develop backend services and APIs using Java (preferred), Python, or Go.
• Design batch and real-time data pipelines using Spark, Flink, Kafka, and Airflow.
• Optimize SQL queries, data models, and distributed data systems.
• Work with cloud platforms (AWS, GCP, or Azure) and container technologies (Docker, Kubernetes).
• Implement CI/CD, monitoring, logging, and performance optimization.
• Collaborate with product and engineering teams on scalable system design and architecture.

Required Qualifications :

• SDE 2 : 3 to 5 years of Backend + Data Engineering experience.
• SDE 3 : 5.5 to 8 years of Backend + Data Engineering experience.
• Strong coding skills in Java (preferred), Python, or Go.
• Excellent SQL and data modeling knowledge.
• Hands-on experience with Airflow, Apache Spark / Apache Flink, or similar technologies.
• Experience building scalable backend services and APIs.
• Good understanding of distributed systems, system design. and scalable architecture.
• Experience with cloud platforms (AWS, Azure, or GCP).
• Hands-on experience with Docker and Kubernetes.
• Strong understanding of CI/CD pipelines.
• Excellent problem-solving and debugging skills.

Good to Have :

• Experience with Snowflake, BigQuery, or Redshift.
• Understanding of Kafka, Kinesis, or Pub/Sub
• Performance optimization of large-scale distributed systems
• FinTech or high-scale distributed systems experience.
• Knowledge of data governance, security, and compliance.

Preferred Candidate Profile :

• Strong Backend + Data Engineering experience
• Experience building scalable production systems
• Strong ownership mindset
• Good system design knowledge
• Experience processing millions of events using Kafka/Spark
• Built APIs supporting data infrastructure
• Production engineering experience

Interview Process :

1. Take-home Coding Assignment (48 Hours)
2. Leadership & Strategy Round (1 Hour)
3. Technical Depth – Data Engineering & Performance (1 Hour)
4. Culture & Values Fit (30 Minutes)

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!