Sr. Data Architect

What s On Your Plate?

As an Analytical Engineer and Data Architect, you will be responsible for:

1. Data Product Development & Ownership

• Build and Maintain: Design, build, and maintain enterprise data warehouses and analytical data marts.

• Translate Needs: Work closely with data scientists, product managers, and business teams to understand their problems and translate complex business requirements into robust data models and data products.

• End-to-End Responsibility: Take full ownership of data products from concept to production, ensuring they are scalable, reliable, and well-documented.

2. Data Quality & Governance

• Enhance Data Quality: Implement and enforce data quality and governance standards throughout the data pipeline, moving checks and enrichment upstream.

• Root Cause Analysis: Proactively identify data quality and governance shortfalls, perform root cause analysis, and implement solutions with monitoring mechanisms.

3. Cross-Functional Collaboration

• Collaborate: Partner with data engineers to ensure smooth data ingestion from source systems and with backend engineers to define and implement data contracts.

• Consult: Serve as a subject matter expert for analytical and business teams, guiding them on best practices for data consumption and model design.

4. Data Pipeline & Modeling Expertise

• Pipeline Management: Design and optimize data pipelines to support data transformation, data structures, metadata, and dependency management.

• Modern Modeling: Apply a variety of data modeling techniques, including modern methods, to build efficient and flexible data solutions.Qualifications

What Did We Order?

• Experience: 5+ years of experience in data management, with a strong focus on analytical engineering.

• Technical Skills: Advanced SQL knowledge and experience with relational and non-relational databases. Proficient in building and optimizing data pipelines and analytical data models.

• Data Modeling: Solid experience with various data modeling techniques, including modern methodologies.

• Center of Excellence: Build best practices and processes supporting data transformation, data structures, metadata, dependency, and workload management.

• Problem-Solving: A strong problem-solver with a figure it out growth mindset and a keep it simple approach.

• Collaboration: Proven ability to work effectively with cross-functional teams in a dynamic environment. Excellent collaborator and communicator who can translate business needs into technical solutions.

• Ownership: A strong sense of ownership and accountability for data products and their impact.

• Education: Bachelor's degree in engineering, computer science, technology, or a similar field.