Azure data bricks +Pyspark

• *Skill:** Azure Databricks

• *Experience:** 6 to 9 years

• *Location:** AIA Kochi

Job Summary

This hybrid role is for an Architect with six to ten years of experience who designs and optimizes Databricks platforms using Databricks SQL Databricks Workflows and PySpark to deliver scalable analytics solutions. The architect collaborates with cross functional teams in a day shift model to transform complex data into actionable insights that support business outcomes and positive societal impact.

Responsibilities

Design robust Databricks based data architectures that efficiently process high volumes of structured and unstructured data while meeting performance security and governance expectations for a large multinational organization.

Develop and optimize complex Databricks SQL queries that power analytics dashboards and reporting solutions ensuring consistent query performance and reliable insight generation for business stakeholders.

Build scalable PySpark data pipelines that cleanse transform and enrich raw data into curated datasets enabling advanced analytics machine learning and data science use cases across multiple domains.

Configure and manage Databricks Workflows to orchestrate end to end batch and incremental data processing jobs ensuring reliable scheduling dependency handling and automated recovery from failures.

Collaborate with data engineers data scientists and product teams in a hybrid work model to translate analytical requirements into well structured Databricks solutions that are maintainable and future ready.

Implement and promote coding standards modular design patterns and reusable PySpark components that increase developer productivity and reduce long term maintenance efforts across projects.

Optimize cluster configurations job parameters and resource usage within Databricks to control cloud costs while sustaining required throughput latency and availability for critical workloads.

Establish data quality validation steps within Databricks Workflows and PySpark jobs that detect anomalies early and prevent propagation of inconsistent or incomplete data into downstream systems.

Document architectural decisions data flow diagrams and technical implementation details in clear and accessible formats to support knowledge sharing and smooth onboarding of new team members.

Collaborate with security and compliance teams to align Databricks configurations access controls and data handling practices with enterprise policies and regional regulations thereby safeguarding sensitive information.

Guide teams in adopting monitoring and alerting practices for Databricks jobs and clusters using logs and metrics enabling proactive issue detection and timely remediation of production incidents.

Engage with business stakeholders to understand analytical goals and translate them into prioritized technical roadmaps that maximize the value of Databricks investments for both the company and the wider community.

Continuously explore new features in Databricks PySpark and supporting cloud services and evaluate their fit for enhancing existing architectures or creating innovative data products that have measurable societal benefits.

Qualifications

Showcase extensive hands on experience designing and implementing Databricks based solutions including data ingestion transformation and consumption layers gained over six to ten years in data engineering or architecture roles.

Demonstrate strong proficiency in Databricks SQL by crafting complex joins aggregations window functions and performance tuned queries that enable advanced reporting and analytics needs.

Apply deep practical knowledge of PySpark for building distributed data processing jobs that handle large datasets including experience with DataFrames Spark SQL and performance optimization techniques.

Exhibit practical expertise with Databricks Workflows by configuring tasks job clusters and dependencies to create resilient data pipelines that are simple to operate and troubleshoot.

Bring solid understanding of data modeling concepts such as star schema and data lakehouse patterns and use these concepts to design reliable and flexible analytical layers within Databricks.

Utilize experience with version control unit testing and continuous integration practices to ensure that Databricks and PySpark codebases remain robust traceable and easy to enhance.

Leverage effective communication and collaboration skills to work with geographically distributed teams in a hybrid environment ensuring shared understanding of requirements timelines and quality expectations.

Certifications Required