Data Engineer Lead

Mandatory Skills:

Python, Apache Spark, Data Quality & Validation, PySpark, SQL, Amazon Kinesis, Apache Kafka, Databricks Workflows, Delta Lake on Databricks

Additional Skills:

AWS S3 + Amazon Aurora + DynamoDB, AWS Glue, AWS Redshift, Data Governance Frameworks, GitHub

Key Responsibilities

• Design scalable data engineering solutions using PySpark and modern distributed data processing frameworks.
• Define data ingestion, transformation, and processing architectures aligned with business and analytical objectives.

Behavioral Competencies

• Demonstrates strong ownership while driving data engineering excellence.
• Collaborate effectively with various teams and business stakeholders to ensure smooth delivery.

Must Have

• Python & Advanced SQL
  • Spark / distributed processing / PySpark
  • Cloud platforms (AWS) Modules like Lambda, Glue, EMR, Dynamo DB, S3, Redshift, EC2, RDS, Step Functions, Cloud Watch, Systems Manager, Event Bridge, DMS
  • SSIS and experience with C# scripting
  • Data pipeline automation

Nice to have

• Kafka/Kinesis streaming

• Basic knowledge of AI

• Power BI

• Postgre SQL

• DBT