Sr Data Engineer

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Nexaminds is looking for a Senior Data Engineer to lead the development, optimization, and scaling of critical data solutions and migration initiatives. The ideal candidate has hands-on expertise building production-grade ingestion and transformation pipelines across operational and analytical systems, and enjoys working in a fast-paced, highly collaborative environment.

Location: MEXICO

Eligibility Notice This position is only open to candidates who are Mexican citizens currently residing in Mexico. Applications from candidates who do not meet this legal and operational requirement will not be considered. We appreciate your interest and encourage you to apply to roles that match your location.

Qualifications we are looking for:

7+ years of experience in data engineering roles delivering production systems, including operating and troubleshooting pipelines in live environments.

Strong SQL skills and experience modeling data for analytics and operational reporting.

Hands-on experience with Snowflake including transformations, backfills, and performance- aware querying.

Strong experience with Postgres or similar operational databases including schema design, bulk loading strategies, and migration concepts.

Experience with streaming/event-driven systems Kafka or equivalent), including understanding topic semantics, consumer lag/backpressure, and safe reprocessing.

Experience building robust batch processes including retries, throttling, idempotency, and operational monitoring.

Demonstrated track record of creating data quality, reconciliation, and auditability controls.

Comfort working in AWS environments and collaborating on IAM and permissions boundaries.

Ability to collaborate across functions and communicate status, risk, dependencies, and decisions clearly.

Advanced English communication skills are required.

Job duties:

Own and deliver production-grade ingestion and transformation pipelines across operational and analytical systems.

Design and run bulk data onboarding and backfills with batching, monitoring, idempotency, and rollback plans.

Establish and execute data quality and validation practices including automated checks, anomaly detection, reconciliation reporting, and provenance/auditability.

Support event-driven data correctness by validating Kafka semantics, consumer behavior, and downstream dependencies during batch operations and migrations.

Build migration tooling and safety rails for datastore and API migrations including parity checks, cutover sequencing, and rollback validation.

Partner with Platform/SRE to ensure production operations are safe, observable, and supportable.

Produce clear artifacts for stakeholders including runbooks, cutover checklists, validation results, and post-mortem follow-ups when needed.