Senior Data Engineer

hermeneutic Investments

. Architect, implement, and operate the complete data infrastructure pipeline for research and trading operations .

Posted 10/16/2026full-timeRemote • Taiwan, Singapore, Hong KongSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in architecting and optimizing data infrastructure pipelines, ensuring data quality and integrity while collaborating with cross-functional teams. Proficient in SQL, Python, and data management best practices, with a strong understanding of data security and performance optimization.

Highest-signal resume keywords

SQL Query OptimizationETL Pipeline DevelopmentData Quality ManagementData Security StandardsCloud Data Services Familiarity

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

SQLPythonJavaScalaApache AirflowDbtApache SparkKafkaDimensional ModelingData Normalization

Soft Skills

Problem-SolvingAnalytical ThinkingClear Communication

Tools & Technologies

SnowflakeRedshiftBigQueryAWSGCPAzureGitTerraformDockerKubernetes

Industry Keywords

Data GovernanceData LineageFinancial ServicesTrading OperationsData Quality Frameworks

Tech Stack

Tools & technologies

AirflowAmazon RedshiftApacheAWSAzureBigQueryDockerETLGoogle Cloud PlatformJavaKafkaKubernetesPythonScalaSparkSQLTerraform

About the role

Key responsibilities & impact

Architect, implement, and operate the complete data infrastructure pipeline for research and trading operations
Design, build, and optimize scalable pipelines for ingesting, transforming, and integrating market data, news feeds, and unstructured data
Ensure data quality, consistency, and real-time monitoring using dbt and data validation libraries
Develop processes to normalize and organize the data warehouse for use across departments
Apply data management practices to ensure scalable, available, and efficient data storage
Maintain data integrity, security, and performance at scale for trading and research infrastructure
Collaborate with research and analytics teams to understand data needs and build frameworks for exploration, analysis, and model development
Create tools for overlaying data from multiple sources
Optimize hardware and software resources to balance performance with cost control
Evaluate and adopt suitable technologies and align systems with data management best practices

Requirements

What you’ll need

Strong problem-solving and analytical thinking
Clear communication skills for cross-functional collaboration
Proficiency in building robust data quality checks for ingested data
Experience identifying anomalies in ingested data
Strong proficiency in writing complex SQL queries and optimizing performance
Proficiency in Python or Java/Scala
Experience building and maintaining complex ETL pipelines with Apache Airflow, dbt, or custom scripts
Strong understanding of dimensional modeling, star/snowflake schemas, and normalization/denormalization principles
Proven experience with Snowflake, Redshift, BigQuery, Synapse, or similar platforms
Expert knowledge of Apache Spark, Kafka, Flink, or similar
Strong understanding of data security and privacy standards
Degree in Computer Science, Engineering, Mathematics, or a related field is preferred
Familiarity with AWS, GCP, or Azure and their data services, proven by relevant certifications is preferred
Experience with data quality frameworks such as Great Expectations or Deequ is preferred
Experience with Git/GitHub or similar for code versioning is preferred
Experience with infrastructure-as-code tools such as Terraform or CloudFormation is preferred
Exposure to Docker and Kubernetes is preferred
Familiarity with data governance, data lineage, and catalog tools is preferred
Hands-on experience with observability and monitoring tools for data pipelines is preferred
Knowledge of machine learning pipelines is preferred
Prior experience in a trading or financial services environment is preferred