Senior Data Engineer
- Role
- Data Engineering
- Experience
- Senior
- Employment
- Full-time
Open to AR 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 datasets. Requires proven experience in data validation, transformation, normalization, and building/maintaining ETL pipelines. 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.
- Design, develop, and optimize end-to-end data pipelines using Azure Databricks
- Refactor legacy code to simplify maintenance, updates, and reuse across multiple use cases.
- Design and build modular, reusable code components to support multiple journeys and reduce duplication.
- Consolidate key KPIs, metrics, and attributes into standardized data structures to enable flexible journey views.
- Build and maintain scalable data models to support current and future journey analytics use cases.
- Ensure data quality, performance, and reliability across data pipelines and analytics datasets.
- Collaborate with analytics and engineering teams to improve data processes and architecture.
- Lead the design and implementation of data ingestion architectures in Snowflake, ensuring scalability and reliability.
- Own the development of end-to-end ELT pipelines integrating multiple data sources.
- Design and manage workflow orchestration using Airflow, ensuring efficient scheduling, monitoring, and dependency management.
- Design and enforce data quality frameworks, including validation rules, testing strategies, and monitoring.
- Build and optimize data pipelines and architectures on AWS (S3, Glue, Lambda, EMR, etc.).
- Develop and enhance data validation and ingestion frameworks.
- Act as a technical leader, guiding best practices in data engineering, orchestration, governance, and pipeline reliability.
- Proactively identify risks, bottlenecks, and data quality issues, and implement mitigation strategies before they impact delivery.
- Contribute to data governance initiatives, including data definitions, lineage, and stewardship practices.
- Drive documentation and continuous improvement of data platform processes.
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.
- 4+ years of experience in Data Engineering.
- Strong experience working with GitHub repositories and version control workflows.
- Hands-on experience developing and maintaining data pipelines in Azure Databricks.
- Strong experience with SQL and large-scale data processing.
- Proven experience refactoring and maintaining legacy codebases.
- Hands-on experience with Apache Spark (PySpark preferred).
- Experience working with dbt for data transformation and modeling.
- Strong understanding of data modeling and reusable component design.
- Experience building scalable data models for analytics and reporting use cases.
- Strong focus on data quality, performance, and reliability.
- Ability to work in cross-functional environments and contribute to continuous improvement.
- Ability to work independently and take ownership of initiatives after receiving high-level direction, driving tasks forward with minimal supervision.
- 3+ years of experience in Data Engineering
- Strong hands-on experience with Snowflake (Must)
- Experience building and maintaining data pipelines in AWS (Must)
- Strong experience building ELT pipelines and data ingestion solutions
- Strong experience with Airflow for workflow orchestration (Must)
- Solid experience with SQL and large-scale data processing
- Experience with data quality, validation frameworks, and governance practices
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 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 annivers anniversaries, and other personal milestones.
- Special day rewards to celebrate birthdays, work anniversies, and other personal milestones.
- 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.
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