We are seeking a Senior Data Engineer with the technical expertise, architectural insight, and problem-solving skills to design, build, and maintain our modern data platform. The ideal candidate will be a hands-on engineer who can deliver at scale, ensure data quality, and work across teams to support both operational and analytical workloads.
Responsibilities- Design, build, and maintain scalable data pipelines (ETL/ELT) for both batch and streaming use cases.
- Implement data validation and integrity frameworks, ensuring accuracy, completeness, and reconciliation across systems.
- Administer and optimize cloud-based data services (AWS, Snowflake, Databricks, etc.).
- Ensure compliance with data governance and regulations (PCI-DSS, GDPR, CPRA, SOX, etc.).
- Deliver proof of concepts and lightweight prototypes to validate architectural improvements.
- Collaborate with business units, audit teams, and stakeholders to align data architecture with organizational needs.
- Support reporting and analytics teams by enabling Tableau (or future-state tools) through well-structured data models and governance.
- Seek out new work proactively and mentor other team members.
- Own projects end-to-end and deliver measurable results.
- Diagnose and resolve complex data issues quickly.
Qualifications- Bachelor's degree in Computer Science, Information Technology, or equivalent experience.
- 10+ years of experience in data engineering.
- Mastery of complicated SQL for loading data, including complex joins, subqueries, windowing functions, and common table expressions.
- Advanced proficiency in Python, developing and supporting robust pipelines, frameworks, and automation solutions.
- Proven history designing and managing Data Warehousing solutions including proper loading techniques, star schema modeling, slowly changing dimensions, and aggregate strategies.
- Strong understanding of OLTP modeling (3NF) and how to denormalize for performance or downstream use.
- Production experience with unstructured and semi-structured data (e.g., JSON, MongoDB, APIs) and integrating it into enterprise data ecosystems.
- Practical expertise implementing and optimizing pipeline orchestration with tools such as Airflow or Prefect.
- Experience implementing and optimizing 3rd party transformation tools such as dbt, Talend, or FiveTran.
- Demonstrated success in delivering scalable solutions using cloud-based data platforms like Snowflake or Databricks.
- Applied experience leveraging AWS data services (S3, Glue, Redshift, Lambda, Secrets Manager, etc.) in secure and cost-effective ways.
- Demonstrated ability to ensure compliance with regulatory requirements including PCI-DSS, GDPR, CPRA, CCPA, and SOX.
- Effective communication skills for engaging both technical and non-technical audiences and collaborating with business and audit stakeholders.
- Proven capability mentoring peers, promoting best practices, and contributing to team growth.
- High adaptability to changing requirements, emerging technologies, and competing priorities.
- Nice to have: Hands-on experience with Data Lake and Lakehouse technologies (e.g., Delta Lake, Iceberg, Hudi) for managing large-scale data assets.
This role is critical to our data engineering practice. We are looking for a hands-on leader who can operate in both structured and fast-changing environments while delivering high-quality, scalable solutions.
