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Lead Software Engineer
Job Description
Key Responsibilities
Data Pipeline Development: Design, develop, and maintain efficient, reliable, and reusable ETL/ELT pipelines using DBT, Snowflake, and related technologies. Data Modeling: Build and optimize data models (star, snowflake schemas) to support analytical and reporting use cases, ensuring data quality, accuracy, and performance. Data Integration: Integrate data from multiple sources (APIs, databases, SaaS platforms, cloud storage) into Snowflake using modern data ingestion tools and frameworks. Performance Optimization: Tune SQL queries, d cost efficiency. Data Governance & Quality: Implement data validation, testing, and monitoring processes to ensure high data quality
Collaboration: Partner with data analysts, data scientists, and business teams to understand requirements, define transformations Automation & CI/CD: Contribute to automation, version control, and deployment best practices using Git, CI/CD pipelines, and DBT Cloud (or similar). Documentation: Maintain clear, comprehensive documentation for pipelines, data models, and workflows. Required Skills & Qualifications
Education: Bachelor's degree in Computer Science, Information Technology, Engineering, or related field. Experience: 4–7 years in data engineering, with at least 3+ years of hands-on experience in Snowflake and DBT. Technical Expertise: Strong SQL skills and experience in building complex transformations. Proficiency in Snowflake architecture (warehouses, micro-partitioning, clustering, query optimization, etc.). Deep understanding of DBT (models, macros, testing, documentation, version control). Experience with cloud platforms such as AWS, Azure, or GCP (e.g., S3, Lambda, Cloud Storage). Familiarity with Python or Scala for data engineering workflows. Knowledge of orchestration tools such as Airflow, Dagster, or Prefect. Experience with Git, CI/CD, and deployment automation. Preferred Skills
Exposure to data warehousing concepts, data lake architectures, and real-time streaming (Kafka, Kinesis, etc.). Familiarity with BI tools such as Looker, Power BI, or Tableau. Knowledge of data governance, security, and compliance standards (GDPR, HIPAA, etc.). Experience working in an Agile environment. Key Attributes
Strong analytical and problem-solving skills. Excellent communication and collaboration abilities. Proactive mindset with attention to detail. Ability to work in fast-paced, cross-functional environments.
Data Pipeline Development: Design, develop, and maintain efficient, reliable, and reusable ETL/ELT pipelines using DBT, Snowflake, and related technologies. Data Modeling: Build and optimize data models (star, snowflake schemas) to support analytical and reporting use cases, ensuring data quality, accuracy, and performance. Data Integration: Integrate data from multiple sources (APIs, databases, SaaS platforms, cloud storage) into Snowflake using modern data ingestion tools and frameworks. Performance Optimization: Tune SQL queries, d cost efficiency. Data Governance & Quality: Implement data validation, testing, and monitoring processes to ensure high data quality
Collaboration: Partner with data analysts, data scientists, and business teams to understand requirements, define transformations Automation & CI/CD: Contribute to automation, version control, and deployment best practices using Git, CI/CD pipelines, and DBT Cloud (or similar). Documentation: Maintain clear, comprehensive documentation for pipelines, data models, and workflows. Required Skills & Qualifications
Education: Bachelor's degree in Computer Science, Information Technology, Engineering, or related field. Experience: 4–7 years in data engineering, with at least 3+ years of hands-on experience in Snowflake and DBT. Technical Expertise: Strong SQL skills and experience in building complex transformations. Proficiency in Snowflake architecture (warehouses, micro-partitioning, clustering, query optimization, etc.). Deep understanding of DBT (models, macros, testing, documentation, version control). Experience with cloud platforms such as AWS, Azure, or GCP (e.g., S3, Lambda, Cloud Storage). Familiarity with Python or Scala for data engineering workflows. Knowledge of orchestration tools such as Airflow, Dagster, or Prefect. Experience with Git, CI/CD, and deployment automation. Preferred Skills
Exposure to data warehousing concepts, data lake architectures, and real-time streaming (Kafka, Kinesis, etc.). Familiarity with BI tools such as Looker, Power BI, or Tableau. Knowledge of data governance, security, and compliance standards (GDPR, HIPAA, etc.). Experience working in an Agile environment. Key Attributes
Strong analytical and problem-solving skills. Excellent communication and collaboration abilities. Proactive mindset with attention to detail. Ability to work in fast-paced, cross-functional environments.
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