Data engineer & Java scala at MNC · Bengaluru (Bangalore) · 4 - 12 years · ₹6L - ₹20L / yr · Posted 16 Oct 2026

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.

Similar jobs (10)
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
1st virtual , 2nd round F2F -
Python pyspark, SQL, data engineer
5+yrs
Bang/hyderabad
immediate to 15days.
1st virtual , 2nd round F2F
Python pyspark, SQL, data engineer
5+yrs
Bang/hyderabad
immediate to 15days.
Data Engineer Short Hiring Post
🚨 Hiring: Data Engineer
🔹 Experience: 5–9 Years
🔹 Location: Bangalore / Hyderabad
🔹 Skills: PySpark, Python, SQL, ETL, CI/CD, Data Modeling
🔹 Process: L1 Virtual → L2 F2F Karat Test
🔹 F2F: Bangalore / Hyderabad Location
🔹 Positions: Immediate requirement
⚠️ Note: Candidates must be available for F2F Karat immediately after L1.
#Hiring #DataEngineer #PySpark #Python #SQL #BangaloreJobs #HyderabadJobs #Mphasis #ImmediateJoiners
🚨 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
1st virtual , 2nd round F2F
Python pyspark, SQL, data engineer
5+yrs
9+yrs
Bangalore/Hyderabad
immediate to 15days.
role: data engineer
Python pyspark, SQL, data engineer
5+yrs
Bangalore/Hyderabad
immediate to 15days.
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 AdTech team and build scalable, high-performance data platforms that power advertising insights and analytics. The ideal candidate will have strong experience in distributed data processing, ETL pipelines, and Big Data technologies, with hands-on expertise in Spark and Scala.
You will work on 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 pipelines for large-scale data processing.
- Build and optimize distributed data applications using Spark and Scala.
- Develop reliable, high-performance data pipelines for batch and streaming workloads.
- Work with large datasets to ensure data quality, consistency, and performance.
- Collaborate with engineering, product, and analytics teams to deliver robust data solutions.
- Optimize data workflows for scalability, reliability, and cost efficiency.
- Deploy and manage data workloads in cloud and containerized environments.
- Troubleshoot production issues and continuously improve platform performance.
Requirements
Candidates who demonstrate:
- 5+ years of experience in Data Engineering or Big Data Engineering.
- Strong hands-on experience with Apache Spark and Scala.
- Experience building and maintaining ETL pipelines.
- Familiarity with Google Cloud Storage (GCS).
- Experience with Kubernetes (K8s).
- Strong SQL skills and understanding of distributed data processing.
- Excellent debugging, problem-solving, and performance optimization skills.
- Strong communication and collaboration skills.
Good to Have
- Experience with AWS and cloud-native data services.
- Familiarity with streaming technologies such as Kafka.
- Experience working on large-scale data platforms or AdTech systems.
- Exposure to orchestration tools such as Airflow.
Benefits
- Best-in-class salary: We hire strong talent and compensate accordingly.
- Proximity Talks: Meet and learn from designers, engineers, product leaders, and AI practitioners.
- Continuous learning: Work with a world-class team and stay close to the latest in AI, engineering, and product development.
- High-impact work: Build AI-first systems and products used at scale by global clients.
About Us
Proximity is the trusted technology, design, and consulting partner for some of the biggest Sports, Media, and Entertainment companies in the world. We’re headquartered in San Francisco and have offices in Palo Alto, Dubai, Mumbai, and Bangalore.
Since 2019, Proximity has built high-impact, scalable products used by millions of users every day. Today, we are a global team of engineers, designers, product managers, and experts solving complex problems and building cutting-edge technology at scale.
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.








