Databricks Engineer

Fluidata Analytics; India; On-site; INR 1,500,000 - 3,000,000; Full time

Benefits offered by this job

Fully Remote work setup

Competitive compensation

Career growth

Continuous learning

Collaborative team environment

Job summary

Fluidata Analytics is seeking a Databricks Engineer to design and maintain scalable data pipelines using Databricks, PySpark, Python, and SQL. You will work across client engagements on AWS, Azure, or GCP and leverage orchestration tools like Airflow or Databricks Workflows to deliver end-to-end data solutions.

You'll collaborate with analytics, BI, and engineering teams, solving complex data modelling, lakehouse architectures, and data quality challenges while accommodating partial

Qualifications

• 3-5 years of experience in Data Engineering.
• Strong hands-on experience with Databricks and Apache Spark.
• Proficiency in PySpark, Python, and SQL.
• Professional services client-facing experience and strong communication skills
• Experience with lakehouses
• Strong understanding of ETL/ELT pipelines and data modelling.
• Working knowledge of at least one major cloud platform (AWS, Azure, or GCP).
• Experience with Airflow, Databricks Workflows, or similar orchestration tools.
• Strong problem-solving and analytical skills.
• Comfortable working directly with international clients and adapting to partial evening-hours overlap.

Responsibilities

• Build and maintain scalable ETL/ELT pipelines and data models using Databricks and PySpark.
• Develop data transformations and processing workflows using Python and SQL.
• Work with Delta Lake and lakehouse architecture.
• Design and maintain data models and data marts for analytics.
• Optimize Spark jobs and SQL queries for performance and scalability.
• Implement data quality, validation, monitoring, and error handling.
• Work across cloud platforms (AWS, Azure, or GCP) depending on the client engagement.
• Use Databricks Workflows, Airflow, or similar tools for orchestration, depending on the client's stack.
• Manage pipeline code through Git-based version control and CI/CD workflows.
• Collaborate with analytics, BI, and engineering teams and directly with international clients to deliver end-to-end data solutions.

Skills

Databricks

Apache Spark

PySpark

Python

SQL

Client-facing communication

ETL/ELT pipelines

Data modelling

Lakehouse architectures

Cloud platforms (AWS/Azure/GCP)

Airflow

Git-based version control

CI/CD for data pipelines

Tools

Airflow

Databricks Workflows

Git

Job description

Fluidata Analytics is an AI-native data and analytics firm. We design and run data systems for clients across 8 industries and 5 continents, turning messy operational data into insights leaders can act on spanning data engineering, analytics, automation, and AI.

Our team is made up of people who learn fast, build with intention, and work directly on challenges that shape how organizations operate and make decisions.

About the Role

We're looking for a Databricks Engineer to build and maintain scalable data pipelines using Databricks, PySpark, Python, and SQL. Because we work across multiple clients, you'll work with different cloud platforms (AWS, Azure, or GCP depending on the engagement) and different orchestration tools so adaptability across environments matters as much as depth in any one stack. The role covers large datasets, cloud data platforms, and end-to-end data engineering workflows.

Job Description

• Build and maintain scalable ETL/ELT pipelines and data models using Databricks and PySpark.
• Develop data transformations and processing workflows using Python and SQL.
• Work with Delta Lake and lakehouse architecture.
• Design and maintain data models and data marts for analytics.
• Optimize Spark jobs and SQL queries for performance and scalability.
• Implement data quality, validation, monitoring, and error handling.
• Work across cloud platforms (AWS, Azure, or GCP) depending on the client engagement.
• Use Databricks Workflows, Airflow, or similar tools for orchestration, depending on the client's stack.
• Manage pipeline code through Git-based version control and CI/CD workflows.
• Collaborate with analytics, BI, and engineering teams and directly with international clients to deliver end-to-end data solutions.

Required Skills

Must-have

• 3-5 years of experience in Data Engineering.
• Strong hands-on experience with Databricks and Apache Spark.
• Proficiency in PySpark, Python, and SQL.
• Professional services client-facing experience and strong communication skills
• Experience with lakehouses
• Strong understanding of ETL/ELT pipelines and data modelling.
• Working knowledge of at least one major cloud platform (AWS, Azure, or GCP) comfort picking up a second is a plus given our multi-client environment.
• Experience with Airflow, Databricks Workflows, or similar orchestration tools.
• Strong problem-solving and analytical skills.
• Comfortable working directly with international (primarily US) clients and adapting to a partial evening-hours overlap.

Nice to have

• Experience with Unity Catalog or similar data governance frameworks.
• Familiarity with Git-based version control and CI/CD for data pipelines (e.g., Databricks Asset Bundles, Terraform).
• Exposure to Structured Streaming or real-time data pipelines.
• Databricks certification (Data Engineer Associate or Professional).

What We Offer

• Fully Remote work setup.
• Daily collaboration and guidance from the management team.
• Competitive compensation packages that reward high performance.
• Exciting career growth paths and a supportive culture that facilitates continuous learning.
• Collaborative team-based environment.
• Prominent client base and existing high-value relationships.
• Best-in-class team of consultants to work alongside you.