Overview
On Site
Contract - W2
Skills
Finance
Lean Methodology
Agile
Testing
Documentation
Accountability
Design Review
Continuous Improvement
Cloud Computing
Optimization
Semantics
Analytics
Artificial Intelligence
Workflow
Business Intelligence
Machine Learning (ML)
Data Quality
Meta-data Management
Sprint
Soft Skills
Collaboration
Mentorship
Data Engineering
Amazon Web Services
Amazon Kinesis
Amazon RDS
Remote Desktop Services
Amazon DynamoDB
Amazon S3
Orchestration
Step-Functions
SQL
Python
PySpark
Scripting
Snow Flake Schema
Databricks
Amazon Redshift
Informatica
Streaming
Use Cases
Data Governance
DICE
Job Details
As part of our transformation, we are evolving how finance, business and technology collaborate, shifting to lean-agile, user-centric small product-oriented delivery teams (PODs) that deliver integrated, intelligent, scalable solutions, and bring together engineers, product owners, designers, data architects, and domain experts.
Each pod is empowered to own outcomes end-to-end-refining requirements, building solutions, testing, and delivering in iterative increments. We emphasize collaboration over handoffs, working software over documentation alone, and shared accountability for delivery. Engineers contribute not only code, but also to design reviews, backlog refinement, and retrospectives, ensuring decisions are transparent and scalable across pods. We prioritize reusability, automation, and continuous improvement, balancing rapid delivery with long-term maintainability.
The Senior Data Engineer plays a hands-on role within the Platform Pod, ensuring data pipelines, integrations, and services are performant, reliable, and reusable. This role partners closely with Data Architects, Cloud Architects, and application pods to deliver governed, AI/ML-ready data products.
Job Responsibilities / Typical Day in the Role
Design & Build Scalable Data Pipelines
Lead development of batch and streaming pipelines using AWS-native tools (Glue, Lambda, Step Functions, Kinesis) and modern orchestration frameworks.
Implement best practices for monitoring, resilience, and cost optimization in high-scale pipelines.
Collaborate with architects to translate canonical and semantic data models into physical implementations.
Enable Analytics & AI/ML Workflows
Build pipelines that deliver clean, well-structured data to analysts, BI tools, and ML pipelines.
Work with data scientists to enable feature engineering and deployment of ML models into production environments.
Ensure Data Quality & Governance
Embed validation, lineage, and anomaly detection into pipelines.
Contribute to the enterprise data catalog and enforce schema alignment across pods.
Partner with governance teams to implement role-based access, tagging, and metadata standards.
Mentor & Collaborate Across Pods
Guide junior data engineers, sharing best practices in pipeline design and coding standards.
Participate in pod ceremonies (backlog refinement, sprint reviews) and program-level architecture syncs.
Promote reusable services and reduce fragmentation by advocating platform-first approaches.
Must Have Skills / Requirements
1) Data Engineering Experience with hands-on expertise in AWS services (Glue, Kinesis, Lambda, RDS, DynamoDB, S3) and orchestration tools (Airflow, Step Functions).
a. 7+ years of experience
2) Proven ability to optimize pipelines for both batch and streaming use cases.
a. 7+ years of experience
3) Knowledge of data governance practices, including lineage, validation, and cataloging.
a. 7+ years of experience
Nice to Have Skills / Preferred Requirements
1) Proven ability to optimize pipelines for both batch and streaming use cases.
2) Knowledge of data governance practices, including lineage, validation, and cataloging.
3) Strong collaboration and mentoring skills; ability to influence pods and domains.
Soft Skills:
1) Strong collaboration and mentoring skills; ability to influence pods and domains.
Technology Requirements:
1) Experience with data engineering, with hands-on expertise in AWS services (Glue, Kinesis, Lambda, RDS, DynamoDB, S3) and orchestration tools (Airflow, Step Functions).
2) Strong skills in SQL, Python, PySpark, and scripting for data transformations.
3) Experience working with modern data platforms (Snowflake, Databricks, Redshift, Informatica).
4) Proven ability to optimize pipelines for both batch and streaming use cases.
5) Knowledge of data governance practices, including lineage, validation, and cataloging.
Additional Notes
Hybrid - 3 days on-site in CA - Burbank.
#LI-NN2
#LI-hybrid
#DICE
Each pod is empowered to own outcomes end-to-end-refining requirements, building solutions, testing, and delivering in iterative increments. We emphasize collaboration over handoffs, working software over documentation alone, and shared accountability for delivery. Engineers contribute not only code, but also to design reviews, backlog refinement, and retrospectives, ensuring decisions are transparent and scalable across pods. We prioritize reusability, automation, and continuous improvement, balancing rapid delivery with long-term maintainability.
The Senior Data Engineer plays a hands-on role within the Platform Pod, ensuring data pipelines, integrations, and services are performant, reliable, and reusable. This role partners closely with Data Architects, Cloud Architects, and application pods to deliver governed, AI/ML-ready data products.
Job Responsibilities / Typical Day in the Role
Design & Build Scalable Data Pipelines
Lead development of batch and streaming pipelines using AWS-native tools (Glue, Lambda, Step Functions, Kinesis) and modern orchestration frameworks.
Implement best practices for monitoring, resilience, and cost optimization in high-scale pipelines.
Collaborate with architects to translate canonical and semantic data models into physical implementations.
Enable Analytics & AI/ML Workflows
Build pipelines that deliver clean, well-structured data to analysts, BI tools, and ML pipelines.
Work with data scientists to enable feature engineering and deployment of ML models into production environments.
Ensure Data Quality & Governance
Embed validation, lineage, and anomaly detection into pipelines.
Contribute to the enterprise data catalog and enforce schema alignment across pods.
Partner with governance teams to implement role-based access, tagging, and metadata standards.
Mentor & Collaborate Across Pods
Guide junior data engineers, sharing best practices in pipeline design and coding standards.
Participate in pod ceremonies (backlog refinement, sprint reviews) and program-level architecture syncs.
Promote reusable services and reduce fragmentation by advocating platform-first approaches.
Must Have Skills / Requirements
1) Data Engineering Experience with hands-on expertise in AWS services (Glue, Kinesis, Lambda, RDS, DynamoDB, S3) and orchestration tools (Airflow, Step Functions).
a. 7+ years of experience
2) Proven ability to optimize pipelines for both batch and streaming use cases.
a. 7+ years of experience
3) Knowledge of data governance practices, including lineage, validation, and cataloging.
a. 7+ years of experience
Nice to Have Skills / Preferred Requirements
1) Proven ability to optimize pipelines for both batch and streaming use cases.
2) Knowledge of data governance practices, including lineage, validation, and cataloging.
3) Strong collaboration and mentoring skills; ability to influence pods and domains.
Soft Skills:
1) Strong collaboration and mentoring skills; ability to influence pods and domains.
Technology Requirements:
1) Experience with data engineering, with hands-on expertise in AWS services (Glue, Kinesis, Lambda, RDS, DynamoDB, S3) and orchestration tools (Airflow, Step Functions).
2) Strong skills in SQL, Python, PySpark, and scripting for data transformations.
3) Experience working with modern data platforms (Snowflake, Databricks, Redshift, Informatica).
4) Proven ability to optimize pipelines for both batch and streaming use cases.
5) Knowledge of data governance practices, including lineage, validation, and cataloging.
Additional Notes
Hybrid - 3 days on-site in CA - Burbank.
#LI-NN2
#LI-hybrid
#DICE
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.