Job Title

Databricks - Lead

Posted On****Location

Noida

Responsibilities

- Define and drive enterprise data engineering strategy aligned with organizational objectives and data modernization initiatives.

- Establish data engineering standards, governance frameworks, and best practices across teams.

- Lead the design of enterprise-scale data processing architectures using PySpark and modern data platform technologies.

- Define enterprise standards for Snowflake and Delta Lake-based data platforms supporting analytical and operational workloads.

- Drive real-time and event-driven data architecture initiatives using Apache Kafka or Amazon Kinesis.

- Establish governance standards for data ingestion, transformation, streaming, and processing frameworks.

- Define workflow orchestration, scheduling, and operational governance standards using Apache Airflow or Databricks Workflows.

- Establish data quality, validation, monitoring, and operational excellence frameworks across data engineering ecosystems.

- Define enterprise standards for data products, data quality ownership, metadata management, discoverability, and trusted business data consumption across the organization.

- Establish architecture standards for modern Lakehouse platforms, data observability, platform engineering, and scalable cloud-native data ecosystems supporting enterprise analytics and AI initiatives.

- Define AI-ready data foundation strategies supporting structured and unstructured data processing, vector-enabled architectures, retrieval patterns, and future GenAI and Agentic AI initiatives.

- Partner with business stakeholders to translate business objectives into scalable data platform capabilities, data products, and enterprise data architecture decisions.





- Drive adoption of AI-assisted engineering practices across data engineering teams to improve developer productivity, code quality, documentation, testing, and delivery effectiveness while maintaining governance standards.

- Lead architecture reviews and ensure data solutions meet scalability, reliability, maintainability, and performance objectives.

- Guide teams on distributed data processing, streaming architectures, modern data platforms, and engineering best practices.

- Identify platform risks, scalability bottlenecks, operational gaps, and architectural challenges while defining mitigation strategies.

- Collaborate with various teams and leadership stakeholders to align data initiatives with organizational objectives.

- Drive continuous improvement initiatives focused on platform maturity, engineering excellence, scalability, reliability, and delivery effectiveness.

Behavioral Competencies

- Demonstrates leadership and accountability in driving data engineering excellence across programs and initiatives.

- Collaborate effectively with various teams and business stakeholders to ensure smooth delivery.

- Promotes a culture of innovation, quality, continuous improvement, and data engineering excellence.

- Apply strategic thinking to address platform challenges, scalability risks, and business priorities.

- Demonstrates solid decision-making while balancing performance, scalability, reliability, and delivery objectives.

- Communicates effectively regarding platform strategy, risks, dependencies, and operational outcomes.

Mandatory Competencies

- Data Science and Machine Learning - Data Science and Machine Learning - Apache Spark

- Data AI - Data Engineering - Data Quality Validation

- Big Data - Big Data - Pyspark

- Database - Database Programming - SQL

- Data AI - Data Engineering - Apache Kafka

- Data Science and Machine Learning - Data Science and Machine Learning - Databricks

- Beh - Communication and collaboration

📌 Databricks - Lead (Noida)
🏢 IRIS SOFTWARE
📍 Noida