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Positions:1 Full Time Experience 5 - 8 Years Data Pipeline Development & Operations Design, build, and operate scalable and reliable data pipelines on the Databricks platform Develop end-to-end data workflows from ingestion through transformation to consumption Implement robust error handling, monitoring, and alerting mechanisms Ensure data pipeline reliability, performance, and maintainability Optimize pipeline performance through efficient Spark job design and cluster configuration Manage and orchestrate complex data workflows using Databricks Jobs and workflows Legacy Code Modernization Refactor legacy code and data pipelines to PySpark for improved performance and scalability Migrate traditional ETL processes to modern ELT patterns on Databricks Assess existing codebases and identify opportunities for optimization and modernization Ensure backward compatibility and data integrity during migration processes Document refactoring approaches and create migration playbooks Collaborate with stakeholders to minimize disruption during code transitions Data Engineering Excellence Implement data quality checks and validation frameworks Design and maintain Delta Lake tables with appropriate optimization strategies Develop reusable code libraries and frameworks for common data engineering tasks Follow software engineering best practices including version control, testing, and CI/CD Participate in code reviews and provide constructive feedback to team members Troubleshoot and resolve data pipeline issues in production environments Collaboration & Knowledge Sharing Work closely with data architects, analysts, and business stakeholders Collaborate with Infrastructure (Infra), Applications (Apps), and Cyber teams Share knowledge and best practices with Team NCS Mentor junior data engineers on PySpark and Databricks technologies Document technical solutions and maintain comprehensive documentation Essential Technical Skills Data Engineering: Strong foundation in data engineering principles, ETL/ELT processes, and data pipeline design patterns PySpark: Proven hands-on experience developing data pipelines using PySpark, including DataFrames API, Spark SQL, and performance optimization Databricks Platform: Practical experience with Databricks workspace, cluster management, notebooks, and job orchestration Workspace AI Agent: Knowledge of Databricks Workspace AI Agent capabilities and integration Data Modelling: Experience implementing data models including dimensional modeling, data vault, or lakehouse architectures Delta Lake: Understanding of Delta Lake features including ACID transactions, schema evolution, and optimization techniques Python: Strong Python programming skills for data processing and automation Additional Technical Skills SQL proficiency for data querying and transformation Experience with cloud platforms (Azure, AWS, or GCP) Understanding of data governance and security best practices Knowledge of streaming data processing (Structured Streaming) Familiarity with DevOps practices and CI/CD pipelines Experience with version control systems (Git) Understanding of data quality frameworks and testing methodologies Professional Experience Minimum 5 years in data engineering or related roles At least 2-3 years of hands-on experience with Databricks platform Proven track record of refactoring legacy code to modern frameworks Experience building and maintaining production data pipelines at scale Background working across multiple data sources and formats Experience in agile development environments Required Certifications - mandatory to have at least one certification Databricks Certified Data Engineer Associate OR Databricks Certified Data Engineer Professional Additional Certifications (Preferred) Databricks Certified Associate Developer for Apache Spark Cloud platform certifications (Azure Data Engineer Associate, AWS Certified Data Analytics, or Google Cloud Professional Data Engineer) Relevant data engineering or big data certifications Soft Skills Strong problem-solving and analytical thinking abilities Excellent communication skills to explain technical concepts clearly Ability to work collaboratively in cross-functional teams Self-motivated with strong attention to detail Adaptable to changing priorities and technologies Client-focused mindset with commitment to quality delivery Open Positions 1 Job Title Senior Data Engineer Work Mode WFO PIf1c414434e99-30511-41302977 .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.