Data Engineer

Job Title : Senior Data Engineer – GCP & Apache Spark

Job Details :

• Number of Positions : 3
• Employment Type : Contract-to-Hire (C2H)
• Experience Required : 5 to 8 Years
• Location : Manyata Tech Park, Bengaluru
• Work Mode : Hybrid – 3 Days in Office per Week
• Preferred Notice Period : Immediate to 15–20 Days
• Project Duration : Initially 6–8 months, with a strong possibility of extension for long-term projects
• Relocation : Candidates from outside Bengaluru can apply if they are willing to relocate.

Role Overview :

We are looking for experienced Senior Data Engineers with strong expertise in Google Cloud Platform (GCP), Apache Spark, BigQuery, and SQL to design, develop, and maintain scalable data pipelines and distributed data processing solutions.

The ideal candidate should have hands-on experience with cloud-based data engineering, end-to-end pipeline architecture, system design, performance optimization, debugging, and CI/CD practices.

Mandatory skills :

Apache Spark (PySpark / Scala), SQL, GCP, BigQuery, GCP Dataproc, Data Pipelines, System Design, CI/CD, Debugging & Problem-Solving.

Key Responsibilities :

• Design, develop, and maintain scalable data pipelines using Apache Spark, PySpark, and/or Scala.
• Build and optimize batch and distributed data processing workflows on GCP.
• Work extensively with BigQuery for data transformation, validation, analytics, and query optimization.
• Develop and maintain data workflows using GCP services, including Dataproc, BigQuery, and Cloud Storage.
• Design end-to-end data pipeline architectures with a focus on scalability, reliability, and performance.
• Write and optimize complex SQL queries for large-scale datasets.
• Troubleshoot and resolve data processing issues, pipeline failures, and performance bottlenecks.
• Implement CI/CD practices for data engineering workflows, testing, and deployments.
• Monitor pipeline performance and identify opportunities for continuous improvement.
• Collaborate with cross-functional teams to understand requirements and deliver reliable data solutions.
• Participate in code reviews, testing, production deployments, and production support.

Mandatory Skills :

• Strong hands-on experience with Apache Spark, including PySpark and/or Scala.
• Strong SQL skills and experience working with large-scale datasets.
• Hands-on experience with Google Cloud Platform (GCP).
• Practical experience with BigQuery and data warehousing concepts.
• Working knowledge of GCP Dataproc.
• Strong understanding of data pipelines, end-to-end workflows, and system design.
• Practical experience with CI/CD and deployment processes.
• Excellent debugging, troubleshooting, and problem-solving skills.

Preferred Skills :

• Experience with Apache Airflow, Google Cloud Composer, or similar orchestration tools.
• Knowledge of Kafka or other streaming and messaging technologies.
• Proficiency in Python for data engineering and automation.
• Experience with Git-based development workflows.
• Knowledge of Linux/Unix environments and shell scripting.
• Familiarity with monitoring, logging, data quality, and governance practices.

Required Qualifications :

• 5 to 8 years of experience in Data Engineering, Big Data Engineering, or a related field.
• Proven experience developing and maintaining scalable data pipelines.
• Strong understanding of distributed data processing and cloud-based data architecture.
• Ability to troubleshoot complex technical issues and optimize pipeline performance.
• Strong communication, collaboration, and ownership skills.

Interview Process :

Round 1 : AI-Based Interview

• Candidates must complete the AI interview as soon as possible after receiving the invitation.

Round 2 : Virtual Interview Drive

• Interviews are planned for the upcoming drive; exact dates will be communicated separately.

Round 3 : Online Client Interview

• Shortlisted candidates will attend the online interview.
• Candidates must be available throughout the day on the scheduled interview date.

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.

1st virtual , 2nd round F2F

Python pyspark, SQL, data engineer

5+yrs

9+yrs

Bangalore/Hyderabad

immediate to 15days.

🚨 HIRING | Data Engineer – Amazon EMR 🔥

We are looking for experienced Data Engineers with strong hands-on expertise in Amazon EMR, Python, and Scala.

📌 JOB DETAILS:

💼 Experience: 6+ Years

💰 CTC: Up to 30 LPA

📍 Location: Bangalore

🔑 MANDATORY SKILLS:

• Amazon EMR

• Python

• Scala

• Data Engineering

📋 KEY REQUIREMENTS:

• 6+ years of relevant experience in Data Engineering.

• Strong hands-on experience with Amazon EMR.

• Strong programming skills in Python and Scala.

• Experience with large-scale data processing and data engineering solutions.

• Strong problem-solving and communication skills.

📩 Interested candidates can share their updated resumes:

📧 pavithra.e @vysystems. com

📞 8 9 2 5 8 0 2 4 5 7

🔄 Know someone who would be a good fit? Tag them or share this post!

#Hiring #DataEngineer #DataEngineering #AmazonEMR #Python #Scala #AWSJobs #BangaloreJobs #ITJobs #TechHiring #HiringNow

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.

Job Description – Data Engineer

Role: Data Engineer

Experience: 9+ Years

Location: Bangalore / Hyderabad

Notice Period: Immediate to 15 Days

Interview Process: 1st Round – Virtual | 2nd Round – F2F

Mandatory Skills

• Python
• PySpark
• SQL
• Azure Data Engineering

Job Description

We are looking for an experienced Azure Data Engineer with 9+ years of experience and strong hands-on expertise in Python, PySpark, SQL, and Azure Data Engineering.

Key Responsibilities

• Develop and maintain scalable data engineering solutions using Azure.
• Build and optimize data processing pipelines using PySpark and Python.
• Write complex SQL queries for data extraction and transformation.
• Work with Azure data services and cloud-based data platforms.
• Perform data processing, transformation, and integration.
• Troubleshoot data pipeline and production issues.
• Collaborate with technical and business teams to deliver data solutions.

Preferred: Immediate to 15 Days joiners.

4 - 10 years of experience in designing and buildingarchitecting highly resilient data platforms 

∙Strong knowledge of data engineering, architecture and data modeling 

∙Experience in platforms like Databricks and Snowflake 

∙Experience on building applications on cloud (AWS or Azure or Google Cloud) 

∙Strong analytical and problem-solving skills 

∙Prior experience in developing data or computation intensive (e.g. grid based) backend applications is an 

advantage 

∙OOP design skills with an understanding or at least personal interest towards the concepts of Functional 

Programming  

∙Willingness to understand and enhance other people’s code, being able to work in an environment where 

developers will oversee and work on wider components also dealing with older “legacy” code 

∙Strong programming skills (Java/ Scala / Python) skills with the willingness to pick up the other language if not 

already mastered at a sufficient level is important 

∙Spring knowledge is an advantage, but in general willingness to learn, work with and even enhance in-house 

developed frameworks is a must 

∙Prior experience in working with Git, Bitbucket, Jenkins, working with PR-s, using JIRA, following the Scrum Agile 

methodology is an advantage 

∙Prior knowledge of financial products is an advantage 

∙Bachelors or Masters in any relevant field of IT/Engineering area is an advantage  

role: data engineer

Python pyspark, SQL, data engineer

5+yrs

Bangalore/Hyderabad

immediate to 15days.

We are hiring for a Data Engineer & Java scala for a full-time opportunity in Bangalore.

Experience: 4–10 Years

Must-Have Skills:

Java / Scala / Python, Data Engineering, Databricks / Snowflake, and Cloud (AWS / Azure / GCP).

We are looking for strong software engineers with hands-on experience in data platform development, scalable backend programming, data integration, and cloud technologies.

Experience in Banking, Capital Markets, Market Risk, or Regulatory Reporting will be an added advantage.

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.