Embedded Systems Data Engineer Location: P.o. Box 2053 United States Employee Type: Contract Location: P.o. Box 2053, MI, US Job Type: Date Posted: March 24, 2026 Pay Range: $55 - $75 per hour Job Description: Role: Embedded Systems Data Engineer Duration: 6-month contract Location: Hybrid Position Overview This role is responsible for designing, building, and maintaining scalable data solutions that enable efficient, accurate collection, processing, and analysis of large volumes of data. The Senior Analytics Engineer will act as a technical force multiplier—translating analytics concepts into production‑ready code, schemas, and validation assets that enable feature engineering teams to adopt data contracts seamlessly. This position sits at the intersection of data engineering, software engineering, and automotive/IoT systems, with a strong emphasis on “analytics as code,” schema governance, and developer enablement. Key Responsibilities - Collaborate with business and technology stakeholders to understand current and future data requirements - Design, build, and maintain reliable, efficient, and scalable data infrastructure for data collection, storage, transformation, and analysis - Plan, design, and implement scalable data solutions, including data pipelines, data models, and supporting applications - Design, implement, and maintain data platforms such as data warehouses, data lakes, and data lakehouse architectures for both structured and unstructured data - Develop analytical tools, algorithms, scripts, and automation to support data engineering workflows - Ensure optimal performance of data solutions and proactively identify improvement opportunities Role Focus: Instrumentation & Data Contracts This role serves as the Technical Implementation Lead for Data Contracts, enabling frictionless adoption by feature engineering teams. Core Responsibilities - Translate measurement plans into production‑ready Data Contracts (YAML/JSON schemas) - Build reusable unit tests (Python / PyTest) for CI/CD pipelines to validate event firing, schema adherence, and payload accuracy - Own schema versioning and evolution in GitHub, ensuring backward compatibility - Optimize instrumentation for vehicle constraints, including LTE bandwidth and 12V battery usage, avoiding unnecessary wake‑ups - Bridge business requirements to hardware‑level signals (TCU/ECU), APIs, and telemetry logic - Provide a “white‑glove” developer experience by delivering ready‑to‑use schemas, tests, and documentation Required Skills - Python - Strong experience with GitHub, YAML, and JSON - Experience treating analytics and data definitions as code - Ability to write automated unit tests and integrate them into CI/CD pipelines Preferred Skills - Telematics - Business Process Support Experience Requirements - 7+ years of data engineering experience Preferred Experience - 5+ years total technical experience with a background in Software Engineering, Data Engineering, or Embedded Systems - 3+ years of code‑based analytics, including Python, GitHub, and CI/CD (beyond SQL or BI tools) - Prior experience in automotive, IoT, or hardware‑constrained environments, including vehicle telemetry, connected devices, or embedded systems - Familiarity with constraints such as bandwidth limitations, battery usage, and device sleep states Education Requirements - Bachelor’s Degree Preferred Education - Master’s Degree Technology Stack - Languages: Python, YAML, JSON, SQL - Tools: GitHub, GitHub Actions, CI/CD, PyTest - Domain: Automotive, IoT Telemetry, Data Contracts, TCU/ECU systems EEO Employer Apex Systems is an equal opportunity employer. We do not discriminate or allow discrimination on the basis of race, color, religion, creed, sex (including pregnancy, childbirth, breastfeeding, or related medical conditions), age, sexual orientation, gender identity, national origin, ancestry, citizenship, genetic information, registered domestic partner status, marital status, disability, status as a crime victim, protected veteran status, political affiliation, union membership, or any other characteristic protected by law. Apex will consider qualified applicants with criminal histories in a manner consistent with the requirements of applicable law. Apex Systems is a world-class IT services company that serves thousands of clients across the globe. When you join Apex, you become part of a team that values innovation, collaboration, and continuous learning. We offer quality career resources, training, certifications, development opportunities, and a comprehensive benefits package. Our commitment to excellence is reflected in many awards, including ClearlyRated's Best of Staffing® in Talent Satisfaction in the United States and Great Place to Work® in the United Kingdom and Mexico. Apex uses a virtual recruiter as part of the application process. Apex Benefits Overview: Apex offers a range of supplemental benefits, including medical, dental, vision, life, disability, and other insurance plans that offer an optional layer of financial protection. We offer an ESPP (employee stock purchase program) and a 401K program which allows you to contribute typically within 30 days of starting, with a company match after 12 months of tenure. Apex also offers a HSA (Health Savings Account on the HDHP plan), a SupportLinc Employee Assistance Program (EAP) with up to 8 free counseling sessions, a corporate discount savings program and other discounts. In terms of professional development, Apex hosts an on-demand training program, provides access to certification prep and a library of technical and leadership courses/books/seminars once you have 6+ months of tenure, and certification discounts and other perks to associations that include CompTIA and IIBA. Apex has a dedicated customer service team for our Consultants that can address questions around benefits and other resources, as well as a certified Career Coach. You can access a full list of our benefits, programs, support teams and resources within our ‘Welcome Packet’ as well, which an Apex team member can provide.

• Diseñas modelos de datos orientados a sistemas no distribuidos de información. • Diseñas procesos de extracción de datos escalables y reutilizables de diferentes orígenes de datos hacia el ecosistema de datos. • Estableces métricas de medición sobre performance del ecosistema de datos. • Estableces modelos de medición enfocados a la calidad de los datos. • Realizas mejoras continuas a la infraestructura que soporta el ecosistema de datos. • Desarrollas la clasificación e investigación de problemas en áreas de BI / Analytics. • Todas las demás funciones que sean asignadas por el líder inmediato y que correspondan a la naturaleza del cargo.

Role Description Our client is a global leader in technology consulting. They are seeking a visionary Lead Data Engineer to spearhead the Data Quality Strategy & Data QA for a SQL to AWS migration that is not going well. You will leverage your Data Engineering and QA Background to do strong root cause analysis to diagnose and fix pipeline failures. - Develop the Data Quality Strategy and Data QA for the SQL to AWS migration - Scalability & High Volume Data - strong permanent coding that can handle millions of records without latency - Database Optimization - optimize data retrieval. Reduce search complexity from linear scans to logarithmic lookups. - Physically structure data to ensure the system remains manageable as it grows. Qualifications - Demonstrated strength in Data Quality Strategy - Exceptional communication skills - Demonstrated strength in Python, Pytest, SQL, Airflow, dbt, AWS, Redshift & S3 - Strong root cause analysis to diagnose and fix data pipeline failures Company Description

• Provision all cloud infrastructure via Terraform: object storage, vector databases, event streaming, Kubernetes, time-series databases, authentication. All reproducible for new tenants. • Design multi-tenant storage: shared vector indices for public data, per-tenant indices for proprietary data. Row-level security or schema-level isolation. • Design per-tenant storage structure with bucket policies enforcing isolation. • Build market data storage pipeline: exchange feeds → event bus → time-series database. • Build monitoring dashboards for data pipeline health across all data sources. • Design feedback data storage: per-tenant schema for feedback events and training data candidates. • Build data archival pipelines for cost-efficient long-term storage. • Automate tenant provisioning: a script that creates a new tenant’s storage, network policies, and service accounts.