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Strong SaaS brand, hybrid, mid-level generalist data role in a metro with broad skill requirements.
Core data engineering skills (SQL, ETL, Snowflake) transfer easily across industries.
Explicit 5+ years requirement and specific data platform and ETL skills increase shortlisting strictness.
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Design, develop, and maintain scalable, efficient data pipelines and ETL solutions using technologies such as Python, Java, Snowflake, AWS, dbt, Matillion, and Airflow.
Collaborate with stakeholders and cross-functional teams to understand data requirements, ensure data quality and validation, troubleshoot issues, and meet SLAs.
Own and improve data architecture, processes, documentation, and operate using Agile Scrum methodologies in a hybrid work model (minimum 2 days per week in-office).
Bachelor’s Degree in Computer Science, Data Analytics, Information Systems, or related field.
Minimum 5 years of experience in dimensional and relational data modeling, data warehouse engineering, and transactional databases.
Experience developing data pipelines using Python or Java.
Work Experience Required: At least 5 years in relevant data engineering roles.
Proven track record with cloud-based big data technologies (AWS, Snowflake, Hadoop, Spark) and commercial ETL tools (dbt, Matillion, SSIS/ADF).
Comfortable working independently and collaborating in Agile Scrum teams, managing job scheduling and monitoring tools like Airflow, Datadog.
Experience in financial, marketing, sales, accounts payable/receivable, or invoicing domains preferred for contextual understanding.