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Broad skillset and popular title but smaller startup brand yields medium applicant competition.
Core data engineering skills (Spark, Python, AWS) are readily transferable across industries.
Explicit 8+ years and many mandatory platform and tooling requirements make shortlisting highly strict.
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Lead design and architecture of scalable, cloud-native data pipelines and architectures using tools like Databricks, PySpark, and Delta Lake.
Provide strategic technical leadership and vision for data engineering initiatives aligned with business goals.
Collaborate cross-functionally to deliver data solutions and mentor junior engineers, ensuring best practices in data management and governance.
8+ years of data engineering experience with diverse architectures and technology stacks.
Proven expertise in designing data warehouses and data lakes, especially on AWS.
Hands-on proficiency with Python for data engineering, and experience with data pipeline orchestration tools such as Airflow, Databricks, DBT, or AWS Glue.
Experience with data processing frameworks (Spark, PySpark), SQL/NoSQL databases (PostgreSQL, Redshift, DynamoDB), and AWS services including IaC tools like Terraform or CloudFormation.
Experienced in leading data engineering projects with a strong focus on cloud-native and scalable architectures.
Proficient in both building and integrating advanced data pipelines, demonstrating a strong technical depth across ETL, data modelling, and warehousing.
Able to communicate complex technical concepts clearly to diverse stakeholders while driving innovation and mentoring others in a dynamic environment.