AWS Data engineer

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 Hiring Post

🚨 Hiring: Data Engineer | PySpark + Python + SQL

We are looking for experienced Data Engineers to join our team!

🔹 Experience: 5 to 9 Years

🔹 Locations: Bangalore / Hyderabad

🔹 Interview Process:

• 1st Round – Virtual

• 2nd Round – Face-to-Face (Karat Test)

🔑 Key Skills:

✅ PySpark

✅ SQL

✅ Python

✅ ETL

📩 Interested candidates can share their updated resume.

#Hiring #DataEngineer #PySpark #Python #SQL #ETL #BangaloreJobs #HyderabadJobs #TechHiring #ImmediateHiring

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.

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

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.

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.
• 
  

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.

Responsibilities and JD

Job Description: We are looking for a Senior Developer with strong expertise in PySpark, Databricks, and Snowflake to build scalable data engineering solutions and enterprise data platforms.

Key Responsibilities:

1. Design, develop, and maintain ETL/ELT pipelines using PySpark, Databricks, and Snowflake.
2. Develop batch and real-time data processing solutions for structured and semi-structured data.
3. Build and optimize Databricks notebooks, workflows, and Delta Lake solutions.
4. Design and implement Snowflake databases, schemas, views, stored procedures, tasks, and streams.
5. Develop scalable data models, data marts, and data warehouse solutions.
6. Optimize PySpark jobs, Databricks workloads, and Snowflake queries for performance and cost efficiency.
7. Implement data quality, validation, governance, and security controls.
8. Collaborate with business stakeholders, architects, and cross-functional teams to deliver data solutions.
9. Manage source control and CI/CD deployments using Git and Azure DevOps.
10. Troubleshoot production issues, perform root cause analysis, and ensure pipeline reliability.
11. Mentor junior team members and participate in code reviews and technical design discussions.

Required Skills: PySpark, Databricks, Snowflake, Python, SQL.

Experience: 5+ years of Data Engineering experience with strong hands-on expertise in PySpark, Databricks, and Snowflake.