Senior Data Engineer
- Role
- Data Engineering
- Experience
- Senior
- Employment
- Full-time
Open to MX only. Set where you work from to check your eligibility.
No BS summary
Senior Data Engineer with 4+ years of experience in data engineering or data analytics. Must have strong hands-on experience with Snowflake, Python for data processing, and SQL for large-scale datasets. Requires proven experience in data validation, transformation, normalization, and ETL pipeline building. Ability to work in cross-functional teams and communicate effectively is essential.
Core skills
Required skills
Optional skills
Required languages
Blend is a premier AI services provider, committed to co-creating meaningful impact for its clients through the power of data science, AI, technology, and people. With a mission to fuel bold visions, Blend tackles significant challenges by seamlessly aligning human expertise with artificial intelligence. The company is dedicated to unlocking value and fostering innovation for its clients by harnessing world-class people and data-driven strategy. We believe that the power of people and AI can have a meaningful impact on your world, creating more fulfilling work and projects for our people and clients.
We’re looking for a Senior Data Engineer to support the design, development, and optimization of data solutions for a high-impact enterprise initiative. This role is ideal for someone with strong hands-on experience in data processing, transformation, and validation, who thrives in building reliable and well-structured datasets for analytics and business workflows. You will work closely with cross-functional teams to ensure data is accurate, standardized, and ready for downstream consumption, contributing to scalable and high-quality data pipelines. What will you be doing? Design, develop, and maintain data pipelines to ingest, transform, and normalize data from multiple sources. Lead the design and implementation of data ingestion architectures in Snowflake , ensuring scalability and reliability. Apply best practices in data validation to ensure accuracy, consistency, and reliability across datasets. Perform data transformation and normalization to support analytics and reporting use cases. Work with large datasets using SQL and Python to process and analyze data efficiently. Ensure data quality by implementing validation rules, checks, and monitoring processes. Collaborate with business and technical stakeholders to understand data requirements and translate them into scalable solutions. Support ETL processes and file handling workflows for structured and semi-structured data. Contribute to documentation and continuous improvement of data processes and standards.
What are we looking for? 4+ years of experience in Data Engineering or Data Analytics roles with strong engineering focus. Strong hands-on experience with Snowflake (Must) . Strong hands-on experience with Python for data processing and transformation. Strong experience with SQL and working with large-scale datasets. Proven experience in data validation, data transformation, and normalization . Experience building and maintaining ETL pipelines and handling data ingestion workflows. Strong attention to detail with a focus on data quality and reliability . Ability to work in cross-functional environments and communicate effectively with stakeholders. Nice to have: Experience working with Salesforce data structures . Familiarity with Tamarac or similar RIA platforms . Experience with Compass or SharePoint integrations . Experience with file handling and ingestion processes (e.g., CSV, JSON, batch files).
Our Perks and 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. 🛫 Travel opportunities to attend industry conferences and meet clients. 👩🏫 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.
What you'll do
- Design, develop, and maintain data pipelines to ingest, transform, and normalize data from multiple sources.
- Lead the design and implementation of data ingestion architectures in Snowflake , ensuring scalability and reliability.
- Apply best practices in data validation to ensure accuracy, consistency, and reliability across datasets.
- Perform data transformation and normalization to support analytics and reporting use cases.
- Work with large datasets using SQL and Python to process and analyze data efficiently.
- Ensure data quality by implementing validation rules, checks, and monitoring processes.
- Collaborate with business and technical stakeholders to understand data requirements and translate them into scalable solutions.
- Support ETL processes and file handling workflows for structured and semi-structured data.
- Contribute to documentation and continuous improvement of data processes and standards.
- Develop and implement Snowflake-based governance solutions that ensure data quality, security, and compliance across enterprise platforms.
- Build automated privacy processes to streamline data protection workflows and reduce manual intervention.
- Support data cleanup initiatives to maintain data integrity and optimize warehouse performance.
- Implement data masking and obfuscation techniques to safeguard sensitive information.
- Assist with JSON field privacy remediation to ensure compliance with data protection standards.
- Collaborate with cross-functional teams to design scalable data architecture and pipeline solutions.
- Contribute to the design and development of scalable data pipelines on AWS (Glue, S3, Athena, Step Functions).
- Build and optimize batch and streaming ingestion pipelines, including CDC-based architectures.
- Support the design and implementation of data models aligned with business and analytics needs.
- Ensure data quality, reliability, and performance across pipelines and datasets.
- Collaborate with cross-functional teams to align technical solutions with business requirements.
- Apply best practices in data engineering, including modular design, reusability, and governance.
- Leverage AI-assisted development tools to improve productivity and code quality.
- Proactively identify risks, bottlenecks, and optimization opportunities across data workflows.
What they require
- 4+ years of experience in Data Engineering or Data Analytics roles with strong engineering focus.
- Strong hands-on experience with Snowflake (Must).
- Strong hands-on experience with Python for data processing and transformation.
- Strong experience with SQL and working with large-scale datasets.
- Proven experience in data validation, data transformation, and normalization .
- Experience building and maintaining ETL pipelines and handling data ingestion workflows.
- Strong attention to detail with a focus on data quality and reliability .
- Ability to work in cross-functional environments and communicate effectively with stakeholders.
- 3+ years of experience in Data Engineering or related disciplines such as data governance, data architecture, or analytics engineering, with hands-on expertise in Snowflake and privacy-focused data solutions.
- Advanced proficiency in Snowflake SQL and data warehousing concepts.
- Strong experience with ETL/ELT frameworks and pipeline development.
- Demonstrated expertise in data privacy implementation and governance.
- Hands-on experience processing and manipulating JSON data structures.
- Solid understanding of cloud data platforms and their architectural patterns.
- Proficiency in automation scripting (Python, Bash, or similar languages).
- Excellent problem-solving skills and attention to detail.
- 5+ years of experience in Data Engineering
- 2+ years in a Lead or Senior capacity
- Strong experience with AWS ecosystem (Glue, S3, Athena, Step Functions) (Must)
- Strong hands-on experience with Python and PySpark (Must)
- Strong hands-on experience with Snowflake (Plus)
- Deep understanding of data pipeline architecture and design patterns
- Experience implementing and scaling CDC (Change Data Capture) patterns
- Strong experience in data modeling for analytics and reporting
- Experience leading initiatives or teams in data engineering environments
- Familiarity with AI-assisted development tools (Plus)
- 4+ years of experience in Data Engineering
- Experience with AWS ecosystem tools such as Glue, Athena, EMR, S3/Lambda, and infrastructure-as-code tools like CloudFormation, Terraform, or CDK, as well as dbt (Must)
- hands-on experience in Snowflake-based ETL pipelines (Plus)
- Solid understanding of data quality, validation frameworks, and governance practices
- Strong communication skills, with the ability to work independently and collaborate across technical and business teams
Benefits
- 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.
- Travel opportunities to attend industry conferences and meet clients.
- Career development plans and mentorship programs to help shape your path.
- Special day rewards to celebrate birthdays, work annivers 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.
- Special day rewards to celebrate birthdays, work anniversies, and other personal milestones.
- Every day lunches! (headquarters): Vegetarian, vegan, gluten and sugar free options.
- Gourmet meals every Friday with our on-site chef!
- All the equipment you need to harness your talent (Macbook and accessories).
- Snacks and beverages available everyday (headquarters).
- After office events, football, tennis and game nights (headquarters).
- Everyone is welcome to join our football league every Wednesday's and Friday's.
- Challenge your teammates to a pool game and win the office's trophy!
- Tennis courts available for friendly matches.
- Not a sports person? Don't worry, we also have chess championships, game and music nights for you to join!
- Learning opportunities: AWS Certifications (we are AWS Partners).
- Study plans, courses and other certifications.
- English Lessons.
- Learn from your teammates on our Tech Tuesdays!
- Mentoring and Development opportunities to shape your career path.
- Anniversary and birthday gifts.
- Great location and even greater teammates!
- At-home medical assistance via EMI (or similar provider) through Asobursatil, available for all employees from AllStar to Analyst level.
- Private healthcare plans for Lead-level roles and above.
- Christmas kit delivered to all employees.
- 1 day off for academic graduation.
- Family Day: 1 day off every semester (must be taken within the same semester).
- Savings incentive program via Asobursatil: Year 1: Blend contributes 50% of your monthly savings. Year 2: Blend contributes 100% of your monthly savings. Year 3+: Blend contributes 150% of your monthly savings. Savings can be withdrawn in July and December.
- Forgivable education loans subject to committee approval and budget availability.
- Requirements: 1+ year at Blend, no disciplinary actions in the past 6 months, successful completion of prior training, and knowledge sharing within 6 months post-training. Retention-based forgiveness schedule applies after program completion.
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What people say about this company
3.8/ 5