Position Summary:
We are seeking a Senior Data Engineer with deep expertise in SQL Server and Google BigQuery to design and implement robust, scalable data solutions. In this role, you’ll be a key contributor to our data architecture strategy, helping to bridge legacy systems with modern cloud-based infrastructure and drive data reliability, accessibility, and performance across the organization.
Key Responsibilities:
- Design, build, and optimize scalable data pipelines across SQL Server and BigQuery.
- Lead efforts to migrate and integrate data between legacy systems (SQL Server) and Google Cloud (BigQuery).
- Develop and maintain complex stored procedures, ETL processes, and data models.
- Optimize SQL queries and data workflows for performance, cost-efficiency, and reliability.
- Collaborate with cross-functional teams including data analysts, data scientists, and business stakeholders to understand data needs and deliver solutions.
- Establish best practices for data architecture, governance, quality, and security.
- Mentor junior engineers and contribute to the continuous improvement of the data engineering function.
- Document technical designs, data flows, and process standards.
Required Qualifications:
- Bachelor's degree in computer science, Engineering, Information Systems, or a related field.
- 5+ years of experience in data engineering or a similar role.
- Advanced experience with Microsoft SQL Server (T-SQL, stored procedures, indexing, SSIS).
- Strong hands-on experience with Google BigQuery and the Google Cloud Platform ecosystem (e.g., Cloud Storage, Dataflow, Pub/Sub).
- Proven track record of building and maintaining high-volume, reliable ETL/ELT pipelines.
- Experience with data warehousing concepts, star/snowflake schema, and data modeling.
- Proficient in a scripting language (e.g., Python or Shell) for automation and data processing.
- Excellent problem-solving, communication, and collaboration skills.
Preferred Qualifications:
- Experience with data orchestration tools (e.g., Apache Airflow, dbt).
- Familiarity with CI/CD pipelines and DevOps in a data context.
- Understanding of cost optimization strategies in BigQuery.
- Experience in agile environments and working with distributed teams.