Senior Data Engineer

• Design, develop, test, and maintain scalable ETL/ELT data pipelines.
• Build and manage enterprise data warehouse solutions using Snowflake.
• Write efficient and maintainable Python programs for data ingestion, transformation, validation, and automation.
• Extract and integrate data from databases, APIs, flat files, and other structured or semistructured sources.
• Develop dimensional data models using concepts such as:
• Star and snowflake schemas
• Fact and dimension tables
• Slowly Changing Dimensions
• Incremental and historical data loads
• Create and maintain ETL jobs, schedules, dependencies, alerts, and recovery procedures.
• Work with orchestration and scheduling tools such as Apache Airflow, Control-M, Azure Data Factory, AWS Glue, or similar platforms.
• Implement data-quality checks, reconciliation processes, audit controls, and exception handling.
• Monitor pipeline performance and troubleshoot failed or delayed data-processing jobs. Optimize Snowflake queries, storage, warehouses, and overall pipeline performance. Use Git for source-code management, branching, merging, pull requests, code reviews, and release management.
• Support CI/CD processes for deploying data pipelines and dbt models across environments.
• Collaborate with business analysts, data analysts, reporting teams, architects, and other stakeholders to understand data requirements.
• Prepare and maintain technical documentation, data mappings, lineage, and operational support procedures
• Participate in Agile ceremonies, sprint planning, estimation, peer reviews, and productionsupport activities.

• 6+ years of professional experience in data engineering, ETL development, or data warehousing.
• Strong understanding of ETL/ELT processes and data warehousing concepts.
• Hands-on experience with Python for data processing and automation.
• Strong hands-on experience with dbt, including:
• Models and materializations
• Sources and seeds
• Tests and documentation
• Macros and Jinja
• Snapshots

Strong experience with Snowflake, including:

• Databases, schemas, tables, and views
• Virtual warehouses
• Stages and file formats
• Query and warehouse performance optimization
• Advanced SQL skills, including joins, CTEs, window functions, stored procedures, and query optimization.
• Experience creating, scheduling, monitoring, and troubleshooting ETL jobs.
• Experience with workflow orchestration tools such as Apache Airflow or an equivalent platform.
• Strong working knowledge of Git, including branching strategies, merge conflict resolution, pull requests, and code reviews. Experience working with structured and semi-structured data formats such as CSV, JSON, XML, Avro, and Parquet.
• Knowledge of data-quality frameworks, logging, monitoring, reconciliation, and errorhandling practices.
• Good understanding of software development life cycle, Agile methodologies, and production-support processes.
• Strong analytical, troubleshooting, communication, and problem-solving skills. Preferred Qualifications
• Experience with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform.
• Experience implementing CI/CD pipelines using GitHub Actions, GitLab CI, Jenkins, Azure DevOps, or similar tools.
• Familiarity with BI and reporting tools such as Power BI, Tableau, or Looke
• Understanding of data governance, metadata management, lineage, security, and access control.
• Relevant Snowflake, dbt, cloud, or data-engineering certifications would be an advantage. Expected Competencies
• Effective collaboration with technical and business stakeholders.
• Ability to mentor junior engineers and contribute to engineering best practices.