Sr. Software Engineer Database
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Protocol Intelligence
Data-driven signals on your job's competitivenessMid-level data engineer with common yet sought Databricks/Spark skills, moderate competition.
Core data engineering skills are transferable, but Databricks/Spark and cloud specifics make fit moderately sensitive.
Explicit 6–8 years requirement plus many mandatory technologies and platforms increases shortlisting strictness.
Job Description
Structured overview of role & requirementsAbout This Role
Design and implement scalable data platforms using Data Lake, Lakehouse, Data Mesh architectures, and modern cloud-native technologies.
Build and optimize batch and real-time data pipelines leveraging Databricks, Apache Spark, Kafka/Kinesis, and cloud services like AWS.
Develop Python-based microservices and RESTful APIs for secure, efficient data sharing and operationalize data quality, monitoring, and observability across pipelines.
Minimum Requirements
6–8 years of experience in Data Engineering or related roles.
Strong proficiency in Python, PySpark, and SQL.
Hands-on experience with Databricks, Apache Spark, Apache Airflow or Databricks Workflows, streaming technologies (Kafka/AWS Kinesis), and AWS cloud services (S3, Glue, Athena, Redshift).
Experience with Infrastructure as Code (Terraform), containerization (Docker), and implementing data quality and monitoring solutions.
Ideal Candidate Profile
Experienced in designing and managing large-scale data architectures including Data Lakehouse and Data Mesh with operational knowledge of modern ETL/ELT frameworks.
Proficient in developing scalable, production-grade data pipelines and APIs using Python and Spark in cloud environments, emphasizing data security and reliability.
Comfortable working in a DevOps-driven environment with IaC, CI/CD, and monitoring practices, enabling robust data platform delivery and maintenance.