Data Engineer

🚀 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.

Sr. Data Engineer 

Location: Pune

Responsibilities 

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

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

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

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

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

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

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

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

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

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

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

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

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

Qualifications 

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

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

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

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

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

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

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

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

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

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

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

Education 

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

Equivalent academic and work experience can be considered. 

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

1st virtual , 2nd round F2F

Python pyspark, SQL, data engineer

5+yrs

9+yrs

Bangalore/Hyderabad

immediate to 15days.

Key Responsibilities

• Design, develop, and maintain scalable ETL/ELT data pipelines using Python and PySpark.
• Work extensively with Databricks and Delta Lake, including Delta tables, MERGE operations, schema evolution, Time Travel, and data optimization.
• Develop, schedule, and monitor Apache Airflow DAGs for workflow orchestration and dependency management.
• Work with Kubernetes for containerized application deployment, orchestration, monitoring, and troubleshooting.
• Develop reusable and optimized Python code following software engineering best practices.
• Optimize Spark jobs for performance, scalability, and cost efficiency.
• Implement data quality checks, error handling, logging, and monitoring mechanisms.
• Use Git for version control, branching, pull requests, code reviews, and collaborative development.
• Troubleshoot pipeline failures, investigate data issues, and implement reliable solutions.
• Collaborate with cross-functional teams to understand business requirements and deliver efficient data solutions.

Required Technical Skills

• Python: Strong programming skills, including data manipulation, exception handling, modular programming, and debugging.
• PySpark: Hands-on experience with Spark DataFrames, Spark SQL, joins, transformations, partitioning, and performance tuning.
• Databricks: Strong understanding of Databricks architecture, notebooks, workflows, compute management, and job execution.
• Delta Lake: Experience with Delta tables, ACID transactions, MERGE/UPSERT, schema enforcement, schema evolution, VACUUM, and Time Travel.
• Apache Airflow: Experience developing DAGs, managing task dependencies, configuring retries, scheduling workflows, and troubleshooting failures.
• Kubernetes: Practical knowledge of pods, deployments, services, ConfigMaps, Secrets, resource management, and troubleshooting containerized workloads.
• Git: Proficiency in branching strategies, pull requests, merging, and resolving conflicts.
• SQL: Strong SQL skills, including complex joins, CTEs, window functions, and query optimization.

Good to Have

• Experience with Docker and containerization best practices.
• Experience with Databricks Unity Catalog and data governance.
• Knowledge of CI/CD pipelines and automated deployments.
• Experience deploying and managing Airflow workloads on Kubernetes.
• Familiarity with Kubernetes monitoring, autoscaling, and resource optimization.
• Experience with cloud platforms such as Azure, AWS, or GCP.
• Understanding of data lakehouse architecture and data modeling.
• Experience with batch and incremental data processing.
• Familiarity with data quality frameworks and automated testing.

Preferred Candidate Profile

• Strong analytical and problem-solving skills.
• Ability to independently design, develop, deploy, and troubleshoot data pipelines.
• Good understanding of distributed data processing and container orchestration.
• Experience working in an Agile development environment.
• Strong communication and collaboration skills.
• 
  

1st virtual , 2nd round F2F

Python pyspark, SQL, data engineer

5+yrs

Bang/hyderabad

immediate to 15days.

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 Summary

We are looking for a skilled and experienced Data Engineer to join our growing data team. The ideal candidate will have strong expertise in Python, PySpark, Data Modeling, and Power BI, with hands-on experience in designing, developing, and optimizing scalable data solutions. The role requires working closely with business stakeholders, data architects, and analytics teams to build robust data pipelines and semantic models that enable data-driven decision-making.

Technical Skills

• Strong hands-on experience in Python and PySpark development.
• Expertise in building and optimizing Data Engineering solutions and ETL pipelines.
• Strong understanding of Data Modeling concepts (Star Schema, Snowflake Schema, Dimensional Modeling).
• Experience with Power BI Data Modeling and Semantic Layer development.
• Proficiency in DAX (Data Analysis Expressions).
• Experience designing and managing Semantic Models in Power BI.
• Strong SQL skills and experience working with large datasets.
• Knowledge of data warehousing concepts and best practices.

Preferred Skills

• Experience with cloud platforms such as Azure, AWS, or GCP.
• Exposure to modern data platforms like Databricks.
• Understanding of data governance and data quality frameworks.

Shift: Second Shift (2:00 PM – 10:00 PM)

Joining: Immediate

About the Role

We are looking for an experienced Data Integration Developer with strong hands-on expertise in SQL, Python, ETL development, Snowflake, and AWS data services.

The ideal candidate should have extensive experience in designing, developing, and maintaining data integration solutions, building data warehouse architectures, and implementing scalable ETL pipelines. Strong SQL development and performance optimization skills are essential for this role.

This is a remote position; however, candidates must be available to visit our Mumbai or Bangalore location for three days during the onboarding process.

Key Responsibilities

SQL Development & Performance Optimization

• Develop complex SQL queries, stored procedures, and data transformation logic.
• Optimize SQL queries for performance, scalability, and efficiency.
• Perform advanced data analysis, validation, and reconciliation.
• Troubleshoot complex SQL and data-related issues.

ETL Development & Data Integration

• Design, develop, and maintain ETL pipelines and data integration workflows.
• Work with SQL and Python for data extraction, transformation, and loading.
• Ensure data accuracy, consistency, and integrity across systems.
• Identify and resolve data pipeline failures and integration issues.

Data Warehousing & Data Modeling

• Design and maintain data warehouse architectures.
• Develop data warehouse schemas based on business requirements.
• Work on dimensional data modeling, including Facts and Dimensions.
• Implement Slowly Changing Dimensions (SCD).
• Support data analysis and business information reconciliation.

Snowflake & AWS

• Develop and manage data solutions using Snowflake.
• Work with AWS services, particularly Amazon S3.
• Manage AWS connections and data access configurations.
• Build and maintain cloud-based data integration solutions.

Python & Apache Airflow

• Develop data processing and automation solutions using Python.
• Build, schedule, and monitor workflows using Apache Airflow.
• Troubleshoot workflow failures and improve pipeline reliability.

Agile & Development Lifecycle

• Participate in Agile development processes, sprint planning, and team discussions.
• Contribute throughout all stages of the software development lifecycle.
• Collaborate with cross-functional teams to understand business and technical requirements.
• Perform root cause analysis and implement appropriate corrective actions.

Required Skills

• Advanced SQL – Strong hands-on expertise is mandatory.
• Python Development
• ETL Development and Data Integration
• Snowflake
• Apache Airflow
• AWS Services, particularly Amazon S3
• AWS Connection Management
• Data Warehouse Architecture
• Data Modeling (Facts & Dimensions)
• Slowly Changing Dimensions (SCD)
• SQL Query Optimization and Performance Tuning
• Data Analysis and Reconciliation
• Troubleshooting and Root Cause Analysis
• Agile Development Methodologies
• Strong understanding of the Software Development Life Cycle (SDLC)

Experience & Qualifications

• 8–10 years of overall experience in Data Engineering / Data Integration / ETL Development.
• Strong professional experience in advanced SQL and Python.
• Hands-on experience with Snowflake, Apache Airflow, and AWS.
• Proven experience designing and developing data warehouse solutions.
• Experience working across the complete product development lifecycle.
• Ability to independently troubleshoot complex technical challenges.

Additional Requirements

• Immediate joiners preferred.
• Must be comfortable working in the second shift (2:00 PM – 10:00 PM).
• Position is remote, with a mandatory three-day onboarding visit to Mumbai or Bangalore.
• Strong communication, analytical, and problem-solving skills.
• Ability to work independently and collaboratively in an Agile environment.

Job Title : AWS Data Engineer / Data Lake Engineer

Experience : 2 to 4 Years

Location : Gurugram Sector 43

Work Mode : 5 Days WFO

Employment Type : Full-Time

About the Role :

We are looking for a skilled AWS Data Engineer with 2 to 4 years of experience in designing, developing, and maintaining data pipelines and cloud-based data platforms.

The candidate will work on an enterprise AWS Data Lake platform that collects data from multiple business systems, processes large datasets, and makes them available for analytics, reporting, and application development.

The ideal candidate should have strong hands-on experience with AWS data services, Python, PySpark, SQL, and Apache Airflow, along with an understanding of data warehousing and API-based data serving.

Mandatory skills :

AWS Data Lake, S3, EMR, Glue, Athena, Redshift, Lambda, RDS, DMS, IAM, Python, PySpark, Apache Airflow, Advanced SQL, ETL / ELT Pipelines, Data Modeling, PostgreSQL, Production Support.

Key Responsibilities :

• Develop, maintain, and optimize data ingestion and ETL / ELT pipelines on AWS.
• Work with AWS services such as S3, Glue, EMR, Athena, Redshift, Lambda, RDS, and DMS.
• Build and manage data processing workflows using Python, PySpark, and Apache Airflow.
• Implement data lake architecture with staging, curated, and consumption layers.
• Write complex SQL queries and perform data transformation, validation, and modeling.
• Work with PostgreSQL and RDS for structured data storage and consumption.
• Support API-based data access using Hasura GraphQL or similar technologies.
• Monitor production data pipelines, troubleshoot failures, and perform root cause analysis.
• Optimize Spark jobs, SQL queries, and data pipelines for performance and cost efficiency.
• Follow AWS security practices, IAM permissions, and data governance standards.
• Collaborate with application, analytics, and business teams to understand data requirements.

Mandatory Skills :

• 2 to 4 years of experience in Data Engineering, AWS Data Engineering, or Data Platform Development.
• Strong hands-on experience with AWS services : S3, Glue, EMR, Athena, Redshift, Lambda, RDS, DMS, and IAM.
• Good experience in Python and PySpark.
• Practical experience with Apache Airflow for workflow orchestration.
• Strong SQL skills and understanding of database concepts.
• Experience in building and supporting ETL / ELT pipelines.
• Understanding of Data Lake architecture, data warehousing, and data modeling.
• Working knowledge of PostgreSQL or similar relational databases.
• Experience handling production pipeline issues and troubleshooting.

Good to Have :

• Experience with Hasura GraphQL or any GraphQL-based API / data-serving platform.
• Knowledge of DBT (Data Build Tool).
• Understanding of Lakehouse architecture and table formats such as Apache Iceberg or Delta Lake.
• Experience with Kafka or other streaming data technologies.
• Knowledge of Amazon Redshift performance optimization, distribution keys, and sort keys.
• Experience with AWS Aurora and API Gateway.
• BFSI, Banking, or Insurance domain experience.

Ideal Candidate Profile :

• Strong hands-on technical and problem-solving skills.
• Experience working on real-time or batch-based production data pipelines.
• Good understanding of data processing, transformation, and storage concepts.
• Ability to troubleshoot technical issues and work independently.
• Good communication and collaboration skills.

Note : Candidates with strong practical experience in AWS Data Lake, PySpark, Airflow, Python, and SQL will be preferred. Real-world project experience is more important than theoretical knowledge or certifications.