IC4 - Sr Data Engineer

Spin - Job Board External; Estado de México; On-site; MXN 900,000 - 1,200,000; Full time

Job summary

Spin - Job Board External is seeking a senior Data Engineer to lead the design, development, and optimization of scalable data solutions. You will mentor engineers, define architectures, and advance data governance, ensuring performance, security, and business value.

Responsibilities include building robust pipelines, collaborating with cross-functional teams, and delivering high-quality data platforms for analytics, reporting, and AI workflows.

Qualifications

• Minimum 5–6 years of data engineering experience with scalable data solutions.
• Advanced SDLC and design pattern knowledge.
• Proven ability to work autonomously and mentor junior staff.
• Strong Python, Java, and data framework expertise.
• Deep knowledge of dimensional modeling and data storage (SQL/NoSQL).
• Experience with batch, near real-time, and real-time pipelines.
• Proficient in data governance, privacy, and security practices.
• Familiarity with API integration and data architectures (Datalake/warehouse).
• Experience with GCP and AWS data services; strong SQL skills.
• Agile project delivery experience (Scrum/Kanban).

Responsibilities

• Lead design and rollout of scalable data pipelines for diverse data types.
• Partner with cross-functional teams to align data architectures with business needs.
• Develop and optimize data models to support analytics, reporting, and AI apps.
• Implement batch, micro-batch, near real-time, and real-time workflows.
• Drive data governance, privacy, and security across data lifecycles.
• Maintain data lake/warehouse architectures and streaming platforms.
• Build data quality metrics and monitoring processes.
• Contribute to all data engineering lifecycle phases with Agile methods.
• Mentor junior and mid-level engineers and foster collaboration.
• Document architectures, processes, and workflows for continuity.
• Integrate APIs and external data sources into existing pipelines.
• Ensure clean, maintainable code via version control and best practices.
• Promote autonomous work culture with proactive problem solving.

Skills

Python

Java

SQL

GCP

AWS

Data modeling

ETL/ELT

NoSQL

Data governance

Git/GitHub/GitLab

Agile/Scrum/Kanban

Tools

GitHub

GitLab

Parquet

JSON

CSV

Job description

Job Family: Decision Sciences > Sub-family: Data Engineering

Reports to (role): Data Engineering Lead I Sr Lead I Manager

Objective of the Role

Leads the design, development, and optimization of advanced data solutions, ensuring scalability, reliability, and alignment with business objectives. This role plays a critical part in defining data architectures, mentoring team members, and driving the adoption of best practices throughout the data engineering lifecycle. With a strong focus on performance, security, and governance, ensures that data solutions contribute to a data-driven culture and deliver measurable business value.

Main Responsibilities

• Lead the design and implementation of robust and scalable data pipelines, enabling efficient ingestion, processing, and storage of structured, semi-structured, and unstructured data.
• Collaborate with cross-functional teams to gather requirements, define technical solutions, and align data architectures with business needs.
• Develop and optimize data models using industry-standard techniques to support analytics, reporting, and AI-driven solutions.
• Implement advanced data processing workflows, including batch, microbatch, near real-time, and real-time pipelines, to ensure seamless data availability and performance.
• Drive the adoption of data governance and privacy strategies, ensuring compliance with security and regulatory requirements throughout the data lifecycle.
• Design and maintain data architectures, contributing to the scalability and reliability of data lakes, warehouses, and real-time streaming platforms.
• Develop monitoring processes and data quality metrics to ensure the reliability, integrity, and completeness of data used for decision-making.
• Actively contribute to all phases of the data engineering lifecycle, adhering to Agile methodologies and ensuring timely delivery of high-quality solutions.
• Mentor and guide junior and mid-level engineers, fostering a collaborative and inclusive team environment.
• Maintain and document data architectures, processes, and technical workflows to ensure continuity and knowledge sharing.
• Lead the integration of APIs and external data sources, optimizing data flows and ensuring compatibility with existing architectures.
• Manage version control and repository practices to maintain clean, reliable, and maintainable codebases.
• Promote an autonomous work culture by encouraging self-management, accountability, and proactive problem-solving among team members.
• Serve as a Spin Culture Ambassador to foster and maintain a positive, inclusive, and dynamic work environment that aligns with the company's values and culture.

Required Knowledge and Experience

• Minimum 5–6 years of experience in data engineering or related fields, with proven expertise in designing and implementing scalable data solutions.
• Advanced technical expertise in software development life cycle (SDLC) methodologies.
• Advanced understanding of design patterns.
• Able to work autonomously and provide support to less qualified professionals.
• Advanced experience in the use of Python, Java, and data-related frameworks.
• Advanced knowledge of dimensional data modeling and advanced techniques in this field, including structured, semi-structured, and unstructured data storage (NoSQL), as well as ETL, ELT construction.
• Advanced knowledge in file processing: CSV, JSON, Parquet, YAML, images, and videos.
• Advanced knowledge in data processing: batch, microbatch, near real-time, and real-time.
• Advanced understanding and experience to contribute and implement data privacy strategies.
• Advanced understanding and experience in implementing and maintaining robust data governance strategies.
• Advanced knowledge in Data Engineering Lifecycle (also known as Data Processing Lifecycle).
• Advanced knowledge in processing different types of internal and external data sources with API management, for subsequent processing, storage, and analysis.
• Advanced knowledge in understanding Architecture: Business, Solutions, Data, etc.
• Advanced mastery in version control: Git, GitHub, GitLab.
• Advanced in business vision (business domain), to sensitively identify how data contributes to the business.
• Advanced mastery and evidence with projects using the Google Cloud Platform (GCP) Stack for Data.
• Advanced mastery and evidence with projects using the Amazon Web Services (AWS) Stack for Data.
• Advanced experience in developing and optimizing SQL and related queries.
• Able to effectively and exceptionally socialize the design of the data solution without supervision.
• Advanced experience in Project Management with Agile methodology (Scrum or Kanban).
• Experience in collaborating on projects to achieve OKRs, warning of risks, and providing business value.
• Advanced competence in transparently communicating project status.