Date Engineer

Job Title: Senior Data Engineer 
Department: Data Engineering / Analytics 

About the Role 

We are seeking a Lead/Senior Data Engineer to join our dynamic data team and play a critical role in building and scaling our modern data platform. This role requires hands-on experience in designing robust data pipelines and transforming data into actionable insights using best-in-class tools and practices. 

You will be responsible for architecting, developing, and maintaining data pipelines that support both batch and real-time data ingestion, transformation, and delivery across the organization. You’ll work closely with data analysts, scientists, and business stakeholders to ensure data quality, performance, and availability. 

Key Responsibilities 

• Design, build, and maintain scalable data pipelines using Fivetran for ingestion, Databricks for transformation & Airflow for Orchestration. 

• Develop real-time data ingestion solutions using AWS Kinesis (Optional). 

• Implement data transformation workflows with dbt (Data Build Tool), leveraging Iceberg table format for optimized performance and versioned data storage. 

• Write efficient and clean code using Python, PySpark, and SQL to support complex data transformations and analysis. 

• Work with large-scale datasets and ensure data quality, lineage, and governance across the pipeline. 

• Optimize pipeline performance, monitor jobs, and resolve data-related issues proactively. 

• Collaborate with cross-functional teams including data scientists, product managers, and engineers to understand data needs and deliver solutions. 

• Experience with any DevOps tool like Git  

Qualifications 

• 5+ years of experience in data engineering or a related field. 

• Proven expertise in data pipeline development and orchestration using tools like Fivetran and dbt. 

• Strong hands-on experience with Databricks, Spark/PySpark, and Apache Iceberg. 

• Experience with real-time data streaming using Kinesis or equivalent (Kafka, etc.) is a plus. 

• Experience with Snowflake is a plus. 

• Advanced proficiency in SQL and Python. 

• Solid understanding of data warehousing concepts, data modeling, and performance tuning. 

• Familiarity with versioned data storage and open table formats like Iceberg. 

• Experience working in cloud environments, particularly AWS. 

• Excellent communication and collaboration skills. 

• Experience with CI/CD for data pipeline deployment. 

• Familiarity with orchestration tools such as Airflow or Databricks Workflows 

• Strong communication and collaboration skills, with a team-first mindset.