Databricks engineer

Employment type –
Full time
Location –
Remote
Working Hours -
4:00 PM – 12:00 AM IST
Experience –
3 – 5 years
Notice period –
Immediate to 30 days
About Fluidata
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, Py Spark, 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 Py Spark.
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 Py Spark, 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.

Work arrangement
Yes