Senior Data Engineer

JLL; Bengaluru; On-site; INR 2,800,000 - 4,500,000; Full time

Job summary

JLL Technologies invites applications for a Senior Data Engineer to design and build scalable, cloud-based data infrastructure. You will drive data pipelines across data lakes, warehouses, and analytics platforms, collaborating with cross-functional teams across the globe.

The role requires 5+ years in data engineering or related fields, with strong Python, SQL, PySpark, and Databricks expertise. Experience with cloud platforms (AWS/Azure/GCP) and CI/CD practices is essential for success.

Qualifications

• Bachelor's degree in Computer Science, Data Engineering, or a related field (Master's degree preferred)
• Minimum 5+ years of experience in data engineering or full-stack development, with a focus on cloud-based environments

Responsibilities

• Design, develop, and maintain scalable and efficient cloud-based data infrastructure using SQL and PySpark
• Collaborate with cross-functional teams to understand data requirements, identify potential data sources, and define data ingestion architecture
• Design and implement efficient data pipeline frameworks, ensuring the smooth flow of data from various sources to data lakes, data warehouses, and analytical platforms
• Troubleshoot and resolve issues related to data processing, data quality, and data pipeline performance
• Stay updated with emerging technologies, tools, and best practices in cloud data engineering, SQL, and PySpark
• Collaborate with data scientists, analysts, and other stakeholders to understand data requirements and deliver data solutions that meet their needs
• Document data infrastructure, data pipelines, and ETL processes, ensuring knowledge transfer and smooth handovers
• Create complex automated tests and integrate them into testing frameworks

Skills

Python

SQL

PySpark

Databricks

Cloud platforms

CI/CD

ETL pipelines

Data warehousing

Data governance

APIs & microservices

Education

Bachelor's degree in CS / Data Engineering or related field

Master's degree preferred

Tools

GitHub

CI/CD tools

Job description

• JLL Technologies is a specialized group within JLL that delivers unparalleled digital advisory, implementation, and services solutions to organizations globally
• We provide best-in-class technologies to bring digital ambitions to life by aligning technology, people, and processes
• Our goal is to leverage technology to increase the value and liquidity of the world's buildings while enhancing the productivity and happiness of those who occupy them

Senior Data Engineer Job Description

About JLL Technologies (JLLT)

• JLL Technologies is a specialized group within JLL that delivers unparalleled digital advisory, implementation, and services solutions to organizations globally
• We provide best-in-class technologies to bring digital ambitions to life by aligning technology, people, and processes
• Our goal is to leverage technology to increase the value and liquidity of the world's buildings while enhancing the productivity and happiness of those who occupy them

What the Job Involves

• We are seeking a Senior Data Engineer who is a self-starter to work in a diverse and fast-paced environment as part of our Enterprise Data team
• This individual contributor role is responsible for designing and developing data solutions that are strategic to the business and built on the latest technologies and patterns
• This is a global role that requires partnering with the broader JLLT team at the country, regional, and global levels by utilizing in-depth knowledge of data, infrastructure, technologies, and data engineering experience

Responsibilities

• Design, develop, and maintain scalable and efficient cloud-based data infrastructure using SQL and PySpark
• Collaborate with cross-functional teams to understand data requirements, identify potential data sources, and define data ingestion architecture
• Design and implement efficient data pipeline frameworks, ensuring the smooth flow of data from various sources to data lakes, data warehouses, and analytical platforms
• Troubleshoot and resolve issues related to data processing, data quality, and data pipeline performance
• Stay updated with emerging technologies, tools, and best practices in cloud data engineering, SQL, and PySpark
• Collaborate with data scientists, analysts, and other stakeholders to understand data requirements and deliver data solutions that meet their needs
• Document data infrastructure, data pipelines, and ETL processes, ensuring knowledge transfer and smooth handovers
• Create complex automated tests and integrate them into testing frameworks

Requirements

Education & Experience:

• Bachelor's degree in Computer Science, Data Engineering, or a related field (Master's degree preferred)
• Minimum 5+ years of experience in data engineering or full-stack development, with a focus on cloud-based environments

Technical Skills

• Advanced expertise in managing big data technologies (Python, SQL, PySpark, Spark) with a proven track record of working on large-scale data projects
• Strong Databricks experience
• Advanced database/backend testing with the ability to write complex SQL queries for data validation and integrity
• Strong streaming and real-time API/service validation including automation
• Experience with automated web services (WSDL) and microservices (REST) using custom scripts and assertions for data validation and data-driven testing
• Experience with cloud platforms such as Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP)
• Proficiency in object-oriented programming and software design patterns
• Experience working in DevOps model, including installing, configuring, and integrating automation scripts on continuous integration tools (CI/CD) and GitHub for real-time test suite execution and troubleshooting
• Experience with Unit, Functional, Integration, User Acceptance, System, and Security testing of data pipelines
• Strong experience in designing and implementing data pipelines, ETL processes, and workflow automation
• Familiarity with data warehousing concepts, dimensional modeling, data governance best practices, and cloud-based data warehousing platforms (e.g., AWS Redshift, Google BigQuery, Snowflake)
• Familiarity with cutting-edge AI technologies and demonstrated ability to rapidly learn and adapt to emerging concepts and frameworks

Core Competencies

• Strong problem-solving skills and ability to analyze complex data processing issues
• Excellent communication and interpersonal skills to collaborate effectively with cross-functional teams
• Attention to detail and commitment to delivering high-quality, reliable data solutions
• Ability to adapt to evolving technologies and work effectively in a fast-paced, dynamic environment