Senior Data Engineer at Blend360

Guadalajara, jalisco, Mexico -

Full Time

Start Date

Immediate

Expiry Date

16 Jul, 26

Salary

0.0

Posted On

17 Apr, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

SQL, Python, Azure Data Factory, Azure Data Lake Storage, Databricks, Snowflake, Data modeling, Medallion architecture, Data engineering, Pipeline development, Power BI, CI/CD, Git, Data governance, PIPEDA compliance, Cloud data warehousing

Industry

Professional Services

Description

Company Description Blend is a premier AI services provider, committed to creating meaningful impact for its clients through the power of data science, AI, technology, and people. We help organisations solve complex business challenges by combining deep domain understanding with modern data and AI capabilities. Our teams work across strategy, analytics, engineering, and product delivery to create scalable, high-value solutions that improve decision-making, efficiency, and growth. Job Description We are looking for an experienced Senior Data Engineer to support the delivery of a foundational Azure data platform for a large telecommunications client. This role will be central to building and operating the ingestion pipelines, Medallion architecture, and data models that underpin operational reporting across the business. The ideal candidate will have strong hands-on experience in cloud data engineering, pipeline development, data modelling, and working with modern cloud data warehousing platform, whether Databricks or Snowflake. This person will work closely with BI Consultants, DevOps Engineers, and Data Governance leads to ensure that data from priority source systems is reliably ingested, transformed, and delivered as trusted, well-governed datasets that support business decision-making and PIPEDA compliance. Responsibilities Design, build, and maintain scalable ingestion pipelines from priority source systems into Bronze, Silver, and Gold layers of the Medallion architecture, covering both batch and incremental load patterns. Complete source-to-target mapping documentation, agree conformed dimensions, taxonomies, and systems of record with the governance workstream, and implement the Gold aggregation layer with KPI metric definitions signed off by business stakeholders. Model and transform business data across the Medallion layers into structures that support operational Power BI reporting, ensuring Silver and Gold layer tables are optimised for the agreed KPI and reporting requirements. Apply and maintain governed data access controls in the chosen cloud data platform, including role-based permissions and any column-level or row-level security required to meet PIPEDA compliance obligations as defined by the governance workstream. Implement robust ingestion, transformation, and data quality processes including automated DQ checks across all Medallion layers, error handling for failed pipeline runs, and end-to-end testing from source systems to the Gold layer. Drive Silver-to-Gold reconciliation sign-off with business stakeholders, ensuring a single agreed definition for every committed KPI and eliminating cross-department reporting discrepancies. Complete and maintain the data dictionary for all platform tables, ensuring data is well-documented, accessible, and aligned to business definitions used by BI developers, report authors, and stakeholders during UAT. Work with architects and client data stakeholders to align designs with enterprise data standards, governance requirements, and long-term maintainability. Produce data engineering runbooks and handover documentation, including pipeline operational guides and technical documentation structured for the client’s internal team to maintain and extend the platform independently. Support deployment of data solutions into controlled Dev, Test, and Production environments. Support continued development of the Medallion architecture in later phases, including additional Bronze, Silver, and Gold datasets, and contribute to pipeline orchestration running reliably on the agreed ingestion schedule. Qualifications Strong hands-on experience with SQL and Python for data processing and transformation. Experience building scalable data pipelines and transformation workflows for large, complex datasets. Strong understanding of data modelling, semantic layer design, and analytical data structures. Experience with Azure data services, including Azure Data Factory for orchestration and ingestion (including Self-Hosted Integration Runtimes for on-premises connectivity) and Azure Data Lake Storage as a landing zone. Hands-on experience with a modern cloud data warehousing or lakehouse platform such as Databricks or Snowflake, including building transformation notebooks or jobs, managing compute, and working within a Medallion or equivalent layered architecture. Experience working with large analytical, transactional, or domain-rich enterprise datasets is highly desirable. Understanding of governed data access patterns, role-based permissions, and compliance controls, including familiarity with PIPEDA or equivalent Canadian data privacy requirements and how these translate into platform-level access design. Familiarity with testing, validation, and monitoring for data quality and reliability. Experience with Git-based CI/CD development workflows. Strong communication skills and ability to work collaboratively with technical and business stakeholders. Nice to have Familiarity with Power BI semantic model design and how Gold layer table structures, naming conventions, and relationships affect downstream report development and performance. Specific experience with Databricks (Unity Catalog, Repos, Delta Live Tables) or Snowflake (Streams, Tasks, Snowpipe) is a strong advantage. Experience working in regulated enterprise environments, ideally in telecommunications or similarly complex data landscapes, with an understanding of data sensitivity classification and PII handling requirements. Experience contributing to knowledge transfer and internal capability enablement. What about languages? Advanced English proficiency required. How much experience must I have? 5+ years of experience in Data Engineering, ideally in cloud-based analytical environments. Additional Information Our benefits: Learning Opportunities: Certifications in AWS (we are AWS Partners), Databricks, and Snowflake. Access to AI learning paths to stay up to date with the latest technologies. Study plans, courses, and additional certifications tailored to your role. Access to Udemy Business, offering thousands of courses to boost your technical and soft skills. English lessons to support your professional communication. 👩‍🏫 Mentoring and Development: Career development plans and mentorship programs to help shape your path. 🎁 Celebrations & Support: Special day rewards to celebrate birthdays, work anniversaries, and other personal milestones. Company-provided equipment. ⚖️ Flexible working options to help you strike the right balance. Other benefits may vary according to your location in LATAM. For detailed information regarding the benefits applicable to your specific location, please consult with one of our recruiters. So what are the next steps? Our team is eager to learn about you! Send us your resume or LinkedIn profile below and we’ll explore working together!

Responsibilities

Design, build, and maintain scalable ingestion pipelines and Medallion architecture layers to support operational reporting. Ensure data quality, governance, and compliance with PIPEDA while collaborating with cross-functional teams to deliver trusted datasets.