python backend developer

Key Responsibilities

• Design, develop, and maintain scalable data pipelines using Python, Hadoop, and Apache Spark.
• Develop efficient data processing and transformation workflows for large datasets.
• Write, optimize, and maintain complex SQL queries and PL/SQL procedures.
• Perform data extraction, transformation, and loading (ETL) from multiple data sources.
• Work with structured and unstructured data to support analytics and reporting requirements.
• Optimize data pipelines and queries for performance, scalability, and reliability.
• Ensure data quality, consistency, security, and integrity across data platforms.
• Collaborate with cross-functional teams to understand business requirements and deliver data engineering solutions.
• Use Git, automation, and CI/CD practices to support development and deployment.

Required Skills

• 4.5–10 years of experience in software development or data engineering.
• Strong proficiency in AWS
• Hands-on experience with Hadoop and Apache Spark.
• Good knowledge of SQL and PL/SQL, including query optimization and stored procedures.
• Experience in ETL development, data processing, and data pipeline design.
• Understanding of data warehousing concepts and distributed computing.
• Strong problem-solving, debugging, and performance-tuning skills.

🚀 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.

Key Responsibilities:

• Deliver new functionalities for calculation platform and for upcoming next gen applications for the Sustainability & Climate Business.
• Monitor and optimize application performance.
• Closely collaborate with Product Management, Research and Product Development, Quality Assurance, Data Operation and IT Infrastructure on all stages of software development life cycle.
• Having very good hands-on working experience in Python and Fast API/Flask/Django or similar Rest API based Frameworks.
• Should have worked on at least one end to end Development project from scratch.
• Worked in a team to implement features and partner with Product Owners, UI/UX Team, Architects, and QA to develop implementation plans with a focus on innovation, quality, sustainability, and delivering value to our business clients.
• Responsible for delivery of high-quality enterprise offerings within schedules.
• The candidate should have excellent problem solving and debugging skills.
• The candidate should have experience with maintenance of legacy, phyton applications and incident management on production systems
• The candidate is expected to have excellent communication and good leadership skills.

Skills and experience that will help you excel:

• Good hands-on working experience in Python.
• Experience in research-oriented calculation frameworks like Numpy and Pandas
• Proficient in RESTful Web Services using Fast API, Django or Flask.
• Good Understanding and hands on experience with SQL/NoSQL Databases
• Good understanding of the importance of testing in software development and the usage of unit testing framework like pytest/unittest.
• Source code control systems, especially Git. - Hands on cloud technologies Google or Azure preferred
• Hands on experience with Azure Databricks, Spark, or similar framework
• Experience in building batch/ETL/integration process on cloud preferred Google, Azure
• Existing domain knowledge of climate products including emissions, physical risk, biodiversity
• Experience in developing and managing microservices on cloud.
• DevOps, and well versed with security practices.
• Exposure and advocate of Open source software
• Exposure to use of AI, LLM to solve business problems is an added advantage.
• Experience In designing solution for data-driven applications
• Strong communication, interpersonal and problem-solving skills
• Bachelors degree in computer science, Mathematics, Engineering, related field, or equivalent experience

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.
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Senior Data Engineer – PySpark & Oracle

Experience: 7+ Years

Location: Bangalore

Notice Period: Immediate to 10 Days

Key Skills:

• Strong expertise in Data Modeling, Data Design & Modernization
• Primary skills: PySpark, Oracle SQL/PLSQL
• Secondary skills: Python, ETL & Data Pipelines
• Experience with Kafka and Hadoop
• Exposure to AWS / Azure / GCP
• Good knowledge of Git and JIRA

Roles & Responsibilities:

• Design, develop, and modernize scalable data models and data architecture.
• Develop and optimize data processing solutions using PySpark and Oracle SQL/PLSQL.
• Build and maintain robust ETL workflows and data pipelines.
• Work with Kafka, Hadoop, and cloud platforms for data processing and integration.
• Perform data transformation, optimization, and performance tuning.
• Collaborate with technical teams on data design, development, testing, and deployment.

1st virtual , 2nd round F2F -

Python pyspark, SQL, data engineer

5+yrs

Bang/hyderabad

immediate to 15days.

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.

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.

1st virtual , 2nd round F2F

Python pyspark, SQL, data engineer

5+yrs

Bang/hyderabad

immediate to 15days.

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.