Mutt Data

Data Engineer Lead dl

Remote Data Engineering role with clear candidate location fit.

PostedJul 19, 2026

Eligible countries1 accepted country

Seniority signalSenior

Work settingRemote

Accepted candidate locations

Argentina

AWS Azure Python Spark

Role overview

Data Engineer Lead dl

Requirements and responsibilities

Readable role content extracted into sections for faster review.

🚀 What We Do

Leveraging our expertise, we build modern Machine Learning systems for demand planning and budget forecasting.
Developing scalable data infrastructures, we enhance high-level decision-making, tailored to each client.
Offering comprehensive Data Engineering and custom AI solutions, we optimize cloud-based systems.
Using Generative AI, we help e-commerce platforms and retailers create higher-quality ads, faster.
Building deep learning models, we enhance visual recognition and automation for various industries, improving product categorization, quality control, and information retrieval.
Developing recommendation models, we personalize user experiences in e-commerce, streaming, and digital platforms, driving engagement and conversions.

🌟 Our Partnerships

Amazon Web Services
Astronomer
Databricks

🌟 Our Values

📊 We are Data Nerds
🤗 We are Open Team Players
🚀 We Take Ownership
🌟 We Have a Positive Mindset

Responsibilities 🤓

Lead end-to-end data migration initiatives, ensuring a smooth, secure, and business-aligned transition.
Define and communicate data architecture decisions, evaluating impacts, risks, and performance considerations across cloud data platforms such as AWS Redshift, Databricks, and related technologies.
Coordinate the discovery and documentation of dependencies, schema inventories, data flows, and critical processes involved in migration efforts.
Own migration and cutover planning, ensuring all activities, validations, and contingency measures are properly defined and executed.
Serve as the primary liaison between engineering, analytics, product, and business stakeholders, facilitating clear communication and alignment throughout the project lifecycle.
Translate technical changes in data pipelines, data warehouses, and platform architecture into clear business impact assessments.
Define and document assumptions, requirements, and acceptance criteria to ensure successful migration outcomes.
Oversee data validation processes to guarantee the accuracy, consistency, and integrity of migrated datasets against source-of-truth systems.
Proactively identify, assess, and mitigate migration-related risks, escalating blockers and dependencies when necessary to maintain project timelines and quality standards.
Drive problem-solving and decision-making in fast-paced, ambiguous environments, helping cross-functional teams stay focused on priorities and deliverables.
Create, maintain, and improve project documentation, including migration runbooks, testing strategies, rollback plans, and operational procedures.
Provide regular project status updates to technical and executive stakeholders, ensuring transparency around progress, risks, and next steps.
Promote data governance, quality, and operational best practices throughout the migration lifecycle.
Align engineers, analysts, and product stakeholders around priorities, timelines, and delivery expectations to ensure successful project execution.

Required Skills 💻

Experience in Data Engineering, including building and optimizing data pipelines.
Experience working with Databricks, Python, and Spark, building batch and streaming.
Intermediate English Level.
Familiarity with a BI visualization tool like Looker or Tableau.