Senior Lead Data Engineer

Overview

Who we are

To save time applying Toyota does not offer sponsorship of job applicants for employment-based visas or any other work authorization for this position at this time.

Who were looking for

At TFS were building next-generation products that redefine mobility for millions of customers worldwide. Were looking for a Sr Lead Data Engineer an individual contributor at the principal level who brings deep expertise in data engineering streaming architectures and analytics platforms combined with technical leadership to make data a reliable scalable foundation for the entire engineering organization.

This isnt a management role. Its for the engineer who thinks in pipelines and data contracts: the one who can design a Lakehouse architecture build a real-time streaming platform ensure data quality at scale and make it all self-service for the teams that depend on it. Youll work at the intersection of backend engineering ML/AI and analytics making sure the data that powers our products models and decisions is trustworthy timely and accessible. If you want to build the data backbone of a modern engineering org not just move files around this is the role.

This position is based in Plano TX. The selected candidate will be expected to reside within a commutable distance of this location.

What youll be doing:

• Serve as the technical authority for data architecture across the organization making high-impact decisions on data lake design streaming topologies storage formats partitioning strategies and data modeling patterns

• Design build and maintain production-grade data pipelines batch and real-time from ingestion and transformation to serving and consumption

• Own the data platform: build and evolve the foundational infrastructure that engineering ML/AI and analytics teams depend on for reliable governed and performant data access

• Partner closely withML/AI engineersto ensure training data feature pipelines and model serving data are accurate fresh and efficiently delivered you are the upstream enabler for every model in production

• Collaborate withbackend and full-stack engineersto design event-driven architectures define data contracts and ensure application data flows cleanly into the data platform

• Lead technical design reviews architecture discussions and RFC processes for data initiatives driving alignment across engineering teams

• Identify and resolve systemic data issues: pipeline failures data quality degradation schema drift latency in streaming systems cost inefficiencies in storage and computing and gaps in data observability

• Define and champion data engineering best practices: data modeling schema evolution data contracts testing strategies lineage tracking cataloging and governance

• Design and implement data quality frameworks validation rules anomaly detection freshness checks and alerting so downstream consumers can trust the data without asking

• Collaborate closely with Engineering Managers Product Data Science and Analytics to shape data roadmaps and ensure the platform evolves with business needs

• Mentor and grow engineers at all levels through code reviews pairing design feedback and technical guidance on data engineering topics

• Contribute to hiring by conducting technical interviews and helping define what great looks like for data engineering at TFS

• Proactively communicate technical risks tradeoffs and recommendations to both engineering and non-technical stakeholders

What you bring

• Bachelors degree in Computer Science Data Engineering Information Systems or related field or equivalent practical experience

• 7 years of software or data engineering experience including 35 years focused specifically on data platform and pipeline engineering at scale with a track record of operating at a principal or staff engineer level

• Deep expertise in designing and buildingdata lake and Lakehouse architectures on AWS including:

  • S3as the foundation for data lake storage with strong opinions on partitioning file formats (Parquet Avro ORC) and lifecycle management

  • AWS Gluefor ETL/ELT jobs crawlers and the Data Catalog

  • Amazon Athenafor serverless SQL analytics over the data lake

  • Lake Formationfor fine-grained access control governance and cross-account data sharing

  • Amazon RedshiftorRedshift Serverlessfor data warehousing and high-performance analytical queries

  • Amazon EMRorEMR Serverlessfor large-scale Spark Hive or Presto workloads

• Production experience withreal-time and streaming data architectures including:

  • Amazon Kinesis(Data Streams Data Firehose) for real-time ingestion and delivery

  • Amazon MSK(Managed Kafka) or self-managed Kafka for event streaming at scale

  • EventBridgeSQS orSNSfor event-driven integration with application services

  • Lambdafor lightweight stream processing and event transformation

  • Apache Flink(via Amazon Managed Service for Apache Flink) or Spark Structured Streaming for stateful stream processing

• Strong proficiency inPythonandSQL you write production-quality pipeline code not just ad-hoc scripts and you can optimize a complex query as fluently as you can design a DAG

• Experience withworkflow orchestrationtools:Step FunctionsApache Airflow(via Amazon MWAA) or similar you know how to build reliable observable and recoverable pipeline DAGs

• Solid understanding ofdata modelingfor both analytical and operational use cases: star schemas slowly changing dimensions wide tables event sourcing and CDC (change data capture) patterns

• Experience withdata quality and governancetooling and practices: Great Expectations Deequ or custom validation frameworks plus data cataloging lineage tracking and access control

• Strong understanding ofInfrastructure as CodeusingAWS CDK CloudFormation or Terraform for data infrastructure

• Experience withobservability and monitoringfor data systems: pipeline health dashboards data freshness tracking SLA monitoring and alerting on failures or anomalies (CloudWatch Datadog or similar)

• Strong understanding ofsecurity best practicesfor data: IAM policies Lake Formation permissions encryption at rest and in transit data masking and PII handling

• Deep experience debugging complex issues across data systems pipeline failures data skew schema mismatches streaming lag and storage cost runaway

• Experience with testing strategies for data pipelines: data validation schema contract testing integration testing and pipeline idempotency

• Strong written and verbal communication you can write a clear RFC lead a design review and explain a data architecture tradeoff to a non-technical stakeholder

Added bonus if you have

• Masters degree in Computer Science Data Engineering or related field

• Experience in the financial services banking or insurance industry

• Experience withopen table formats: Apache Iceberg Delta Lake or Apache Hudi for ACID transactions time travel and schema evolution on the data lake

• Experience withfeature storedesign and implementation for ML/AI use cases (SageMaker Feature Store Feast or custom)

• Familiarity withdbtor similar transformation frameworks for analytics engineering and data modeling

• Experience withreal-time analyticsserving layers: Amazon OpenSearch DynamoDB or ElastiCache for low-latency data access

• Experience designingmulti-account AWS data architectureswith proper governance and guardrails (AWS Organizations Control Tower cross-account data sharing via Lake Formation)

• Hands-on experience withdata meshordata productpatterns decentralized ownership with centralized governance

• Experience withCDC (change data capture)tools: AWS DMS Debezium or similar for streaming database changes into the data lake

• Experience withcost optimizationfor data workloads: storage tiering compute right-sizing spot instances for Spark and query optimization

• Experience withGenAI data pipelines: preparing training datasets building RAG knowledge bases embedding generation and vector store population

• AWS certifications (Data Analytics Specialty Solutions Architect Database Specialty)

• Experience withCI/CD pipelinesfor data infrastructure and pipeline deployment (CodePipeline GitHub Actions or similar)

• Experience contributing to or maintaining open-source data engineering projects

• Experience defining engineering standards writing ADRs or leading org-wide technical initiatives

What well bring
During your interview process our team can fill you in on all the details of our industry-leading benefits and career development opportunities. A few highlights
include:

• A work environment built on teamwork flexibility and respect

• Professional growth and development programs to help advance your career as well as tuition reimbursement

• Team Member Vehicle Purchase Discount

• Toyota Team Member Lease Vehicle Program (if applicable)

• Comprehensive health care and wellness plans for your entire family

• Toyota 401(k) Savings Plan featuring a company match as well as an annual retirement contribution from Toyota regardless of whether you contribute

• Paid holidays and paid time off

• Referral services related to prenatal services adoption childcare schools and more

• Tax-Advantaged Accounts (Health Savings Account Health Care FSA Dependent Care FSA)

• Relocation Assistance (if applicable).

Belonging at Toyota

Our success begins and ends with our people. We embrace all perspectives and value unique human experiences. Respect for all is our North Star. Toyota is proud to have 10 different Business Partnering Groups across 100 different North American chapter locations that support team members efforts to dream do and grow without questioning that they belong.

Applicants for our positions are considered without regard to race ethnicity national origin sex sexual orientation gender identity or expression age disability religion military or veteran status or any other characteristics protected by law.

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