Job Description Overview:
Designs, develops, tests, deploys and supports complex data pipelines, data products and data management solutions across modern cloud, warehouse and lakehouse platforms. Applies software-engineering, data-modeling and distributed-processing practices to deliver secure, reliable, maintainable and cost-conscious solutions. Works independently on defined outcomes, collaborates across technology and data teams, and contributes to reusable engineering patterns and continuous improvement.
Primary Responsibilities:
Design, develop, test, deploy and support complex production data pipelines and data products with guidance on broader architecture where needed.
Develop maintainable data-processing and transformation solutions using SQL and programming languages such as Python.
Implement solutions using Snowflake, Databricks, dbt and comparable modern data platforms and frameworks.
Build data solutions across relational databases, cloud object storage, warehouse and lakehouse architectures, including open table formats such as Apache Iceberg where applicable.
Develop batch, micro-batch, CDC and streaming pipelines using approved enterprise patterns and technologies such as Apache Kafka or comparable platforms.
Integrate data from relational and mainframe systems, APIs, event streams, SaaS platforms, files and object storage.
Apply normalized, dimensional and denormalized data-modeling techniques appropriate to analytical and data-product requirements.
Tune SQL, Snowflake and Databricks/Spark workloads for performance and cost using appropriate partitioning, parallelism and compute practices.
Implement data quality, reconciliation, metadata, lineage, logging, monitoring and alerting controls.
Build pipelines that support error handling, restartability, idempotency, retry, replay and recovery using established patterns.
Participate in production support, incident resolution and root-cause analysis; implement corrective actions to prevent recurrence.
Use Git, peer review, automated testing, CI/CD and environment promotion practices for data engineering delivery.
Develop and reuse shared components, templates and patterns; contribute improvements to engineering standards and documentation.
Participate in technical design and code reviews, identify implementation risks and escalate architectural concerns appropriately.
Partner with product owners, architects, governance, cybersecurity, platform and consuming teams to clarify requirements and deliver supported solutions.
Contribute to technical epics and stories, estimate assigned work, identify dependencies and deliver through Agile practices.
Provide day-to-day technical assistance and informal mentoring to less-experienced engineers. This role does not have direct people-management responsibility.
Understand and adhere to the Company’s risk and regulatory standards, policies and controls in accordance with the Company’s Risk Appetite.
Design, implement, maintain and enhance internal controls to mitigate risk on an ongoing basis. Identify risk-related issues requiring escalation to management.
Maintain internal control standards, including timely implementation of internal and external audit points and issues raised by external regulators, as applicable.
Promote an environment that supports belonging and reflects the Company brand.
Complete other related duties as assigned.
Education and Experience Required:
Associate’s degree and a minimum of 5 years’ systems analysis and/ or application development work experience or Bachelor's degree and a minimum of 3 years’ systems analysis and/ or application development work experience. In lieu of degree, a combined minimum of 7 years’ education and/or relevant work experience, including a minimum of 3 years’ systems analysis and/or application development work experience
Strong SQL skills, including development of complex transformations and analytical queries.
Experience with Python or another general-purpose programming…