Job Description
Principal / Lead Data Engineer (Data bricks)

Location:
Remote (India) Exp: 10+ Years Role Overview We are seeking a highly experienced and motivated Principal / Lead Data Engineer to join our dynamic data platform team in either our Gurgaon or Chennai office. The successful candidate will be a critical player in designing, building, and optimizing our next-generation data architecture and pipelines. This role requires expert-level proficiency in PySpark and SQL, alongside a proven track record of architecting scalable, high-performance ETL/ELT processes. You will transform vast amounts of raw data into high-quality, actionable insights for analytics, reporting, and Machine Learning. Given the seniority of this role, we are looking for a seasoned leader and immediate joiner who can hit the ground running and contribute significantly from day one.

Key Responsibilities
Data Pipeline Development & Optimization
• Design and Build: Architect, develop, and maintain robust, scalable, and fault-tolerant ETL/ELT pipelines for ingesting data from diverse sources (e.g., databases, APIs, streaming sources) into our data lake and data warehouse.
• PySpark Expertise: Write and optimize complex data transformation jobs using PySpark and the Spark DataFrame API to process petabytes of structured and unstructured data efficiently.
• SQL Mastery: Utilize Advanced SQL for complex querying, data manipulation, stored procedures, performance tuning, and optimizing database schema design in relational and analytical databases.
• Data Quality & Governance: Implement data validation, cleansing, and monitoring routines to ensure high data quality, integrity, and adherence to security and governance standards. Architecture and Infrastructure
• Data Modeling:



Design and implement optimal data models (e.g., Dimensional Modeling, Data Vault, Snowflake Schema) for our data warehouse to support business intelligence and analytical needs.
• Cloud Integration: Drive cloud-native data solutions primarily leveraging Azure Databricks, Azure Data Lake, and Synapse (or comparable frameworks like AWS S3/Redshift and Google BigQuery) to build and deploy data solutions.
• Automation: Implement orchestration tools like Apache Airflow, Azure Data Factory, or AWS Step Functions to automate data workflows and manage pipeline dependencies. Collaboration and Operational Excellence
• Cross-Functional Leadership: Collaborate closely with Data Scientists, Data Analysts, Product Managers, and Business Stakeholders to understand data requirements and translate them into high-level technical specifications.
• Monitoring & Support: Monitor, troubleshoot, and resolve critical issues in production data pipelines, ensuring maximum uptime and timely data delivery.
• Best Practices & Mentorship: Lead code reviews, enforce strict coding standards, mentor junior engineers, and contribute to the continuous improvement of development and deployment practices (CI/CD, Git). Required Technical Skills (Mandatory)
• Certification: Active Azure Databricks Certification (e.g., Databricks Certified Data Engineer Associate/Professional).
• PySpark: Expert-level,



hands-on experience in developing, tuning, and optimizing large-scale data processing applications using PySpark (Python for Apache Spark).
• SQL: Mastery of Advanced SQL (including window functions, complex joins, stored procedures, and query performance tuning) across various database systems (e.g., Snowflake, Redshift, PostgreSQL).
• Programming: Robust proficiency in Python for scripting, automation, and general data manipulation libraries (e.g., Pandas).
• Big Data Architecture: Deep understanding of Big Data concepts, distributed systems architecture, data lakes, and modern data warehousing principles.
• ETL/ELT: Proven experience in designing and implementing enterprise-grade ETL/ELT pipelines. Preferred Qualifications (Good to Have)
• Hands-on experience with wider Azure ecosystem components (Azure Data Factory, Azure Synapse, Key Vault).
• Familiarity with workflow orchestration tools like Apache Airflow.
• Experience with real-time/streaming data processing (e.g., Spark Structured Streaming, Kafka, or Event Hubs).
• Advanced knowledge of Data Governance, Data Cataloging, and Data Security best practices.
• Candidate Profile
• Educational Background: Bachelor’s or Master’s degree in Computer Science, Engineering, or a related quantitative field.
• Mindset: Proactive, self-motivated, strategic thinker with a strong sense of ownership and urgency.
• Communication: Excellent verbal and written communication skills to articulate complex technical concepts to non-technical stakeholders and executive leadership.
• Availability: Must be an Immediate Joiner or have a short notice period (7 days maximum).

📌 Data Engineer Databricks (India)
🏢 solve IT consultant
📍 India