Data Engineer

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

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.

5

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

6

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.

7

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

8

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.

5

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

6

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.

7

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

8

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

🚀 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 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 Summary

We are looking for an experienced Data Engineer to build and optimize scalable data pipelines and AWS-based data platforms.

Key Responsibilities

• Develop and maintain data pipelines using Python and PySpark.
• Design and implement data lakehouse solutions on AWS.
• Build and manage data workflows using AWS Glue, EMR, ECS, and Step Functions.
• Work with DynamoDB and other data storage technologies.
• Monitor data pipelines and ensure data quality, performance, and reliability.
• Collaborate with stakeholders and engineering teams to deliver data solutions.

Mandatory Skills

• 5+ years of overall professional experience.
• 3+ years of experience in data platforms and big data technologies.
• Strong proficiency in Python and PySpark.
• Hands-on experience with AWS EMR, ECS, Glue, Step Functions, and DynamoDB.
• Experience designing and implementing AWS data lakehouse architectures.

Good to Have

• Apache Spark and Apache Iceberg.
• AWS Lambda, EventBridge, Kinesis, and Redshift.
• Experience with real-time data pipelines and event-driven architectures.

We are hiring experienced Data Engineers for a permanent opportunity in Bangalore.

Job Description

Role: Data Engineer

Location: Bangalore

Experience: 6+ Years

Key Responsibilities:

• Design, develop, and maintain scalable data processing pipelines using Amazon EMR.
• Develop data engineering solutions using Python and Scala.
• Process and transform large volumes of structured and unstructured data.
• Build and optimize distributed data processing workflows for performance and scalability.
• Work with big data technologies and cloud-based data processing environments.
• Perform data integration, validation, transformation, and quality checks.
• Troubleshoot data pipeline issues and optimize processing performance.
• Collaborate with data architects, analysts, and development teams to deliver reliable data solutions.

Required Skills:

• Strong hands-on experience with Amazon EMR.
• Proficiency in Python and Scala.
• Experience in big data processing and data pipeline development.
• Good understanding of distributed computing and data transformation techniques.
• Experience in performance tuning and troubleshooting data processing jobs.
• Strong analytical, problem-solving, and communication skills.

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 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.

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)