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.