AWS Engineer

Fractal Analytics; Bengaluru; On-site; INR 1,200,000 - 2,000,000; Full time

Job summary

Fractal Analytics in Bengaluru is seeking a Data Engineer to design, build, and optimize scalable AWS Databricks data pipelines with PySpark and SQL.

You will work with data scientists, analysts, and architects to ensure reliable data delivery, governance, and cost-efficient Spark workloads in a cloud environment.

Qualifications

• Bachelor’s degree in Computer Science, Information Systems, Engineering, or related field.
• 3–4 years of hands-on Data Engineering experience.
• Strong AWS Databricks expertise with PySpark and SQL.
• Experience with Delta Lake and modern lakehouse concepts.
• Familiarity with AWS services (S3, RDS, Glue, Lambda, Redshift) and CI/CD.

Responsibilities

• Design, develop, test, and deploy scalable ETL/ELT pipelines using AWS Databricks, PySpark, and SQL.
• Build and maintain data pipelines ingesting data from S3, RDS, APIs, and external sources.
• Implement Delta Lake solutions for batch and streaming processing.
• Optimize Spark workloads for performance, scalability, and cost efficiency.
• Ensure data quality, governance, and security with monitoring and validations.
• Collaborate with analysts, modelers, and stakeholders to support reporting and analytics.
• Support orchestration with Airflow, Glue Workflows, or Step Functions and CI/CD with Git/CodePipeline.
• Apply data governance metadata management and access controls.

Skills

AWS Databricks

PySpark

SQL

Delta Lake

Data pipelines

ETL/ELT pipelines

Data governance

Cloud data platforms

Education

Bachelor’s degree in Computer Science, Information Systems, Engineering

Tools

Amazon S3

Amazon RDS

AWS Glue

AWS Lambda

Amazon Redshift

Apache Airflow

Git

CodePipeline

CodeBuild

Delta Lake

Job description

It’s fun to work in a company where people truly BELIEVE in what they are doing!

We’re committed to bringing passion and customer focus to the business.

Job Description

Role Summary

We are looking for a highly motivated Data Engineer with 3–4 years of hands-on experience in AWS Databricks to join our data platform team. The role involves working closely with data scientists, analysts, architects, and business stakeholders to design, build, and maintain scalable, high-performance data pipelines and data solutions within the AWS cloud environment.

The ideal candidate should have strong expertise in Databricks, PySpark, SQL, Delta Lake, and AWS data services, along with a good understanding of modern data engineering practices, orchestration, and data governance.

Key Responsibilities

• Design, develop, test, and deploy scalable ETL/ELT pipelines using AWS Databricks, PySpark, and SQL.
• Build and maintain robust data pipelines for ingesting and processing structured and unstructured data from multiple sources such as Amazon S3, Amazon RDS, APIs, and external platforms.
• Implement and manage Delta Lake solutions to support both batch and streaming data processing.
• Optimize Spark workloads for performance, scalability, and cost efficiency.
• Ensure data quality, consistency, reliability, and integrity through validations, monitoring, exception handling, and reconciliation checks.
• Collaborate with cross-functional teams including data analysts, data modelers, architects, and business stakeholders to support reporting, analytics, and downstream consumption.
• Support orchestration and scheduling of pipelines using tools such as AWS Glue Workflows, Apache Airflow, or AWS Step Functions.
• Participate in CI/CD implementation and code deployment processes using Git, AWS CodePipeline, and CodeBuild.
• Apply best practices related to data governance, metadata management, security, and access control.
• Support production issue resolution, ad hoc data requests, and continuous improvement of the data platform.

Required Skills and Qualifications

• Bachelor’s degree in Computer Science, Information Systems, Engineering, or a related discipline.
• 3–4 years of experience in Data Engineering or a similar role.
• Strong hands‑on experience with AWS Databricks.
• Good proficiency in PySpark and SQL for large‑scale data processing.
• Experience working with Delta Lake and modern lakehouse concepts.
• Familiarity with AWS services such as Amazon S3, Amazon RDS, AWS Glue, AWS Lambda, and Amazon Redshift.
• Knowledge of batch and streaming data architectures.
• Experience with workflow orchestration, monitoring, and debugging of pipelines.
• Understanding of version control and CI/CD practices.
• Awareness of data security, governance, and metadata management principles.
• Strong analytical, problem‑solving, and communication skills.

Preferred Qualifications

• Experience in building data pipelines in enterprise‑scale cloud data platforms.
• Familiarity with real-time / streaming frameworks and event‑driven architectures.
• Exposure to data quality frameworks, reconciliation processes, and operational monitoring.
• Working knowledge of infrastructure automation and cloud deployment best practices.
• Experience collaborating in agile delivery environments.

Key Competencies

• Strong problem‑solving orientation
• Attention to detail and focus on data quality
• Ability to work in a fast‑paced, collaborative environment
• Good stakeholder communication and coordination skills
• Ownership mindset with a focus on delivery excellence

Nice to Have

• Experience with data modeling and analytics enablement
• Exposure to DevOps and platform engineering practices
• Certification in AWS or Databricks technologies

Why Join Us

This role offers the opportunity to work at the forefront of cloud‑based data engineering, partnering with talented data scientists, analysts, and architects to build scalable, high‑impact data solutions on AWS Databricks.

If you like wild growth and working with happy, enthusiastic over‑achievers, you’ll enjoy your career with us!