About the Role

GlossGenius’s Data Engineering team owns the data platform which is a collection of technology, processes and services that enables data worker personas like analytics engineers, analysts, data scientists, product managers in the company to work with data efficiently and securely at scale and help drive insights.

As a Sr Data Engineer, you will be responsible for designing, building and maintaining data platform capabilities that directly impacts time to reliable insights. You will empower data workers in the company with tools and services that help eliminate frictions so they can work with data efficiently across the value chain and make sure data is secure, compliant with privacy regulations, aligns with SLAs, has high quality and associated context.

What You’ll Do

• Design, build and maintain ingestion pipelines ensuring timely and quality data
• Design, build and maintain our data stack and platform capabilities on transformation, governance, metadata management and data observability
• Ensure operational excellence via automations, cost controls, efficient data ops workflows
• Ensure data platform and data handling practices are secure and compliant with regulations 
• Partner with stakeholders - analytics engineers, analysts, data scientists, product managers to understand frictions and quality of life improvements
• Develop and maintain comprehensive documentation for both technical and non technical audiences
• Improve, manage, and teach standards for code maintainability and performance in code submitted and reviewed
• Collaborate with team members to identify requirements, define successful outcomes, and deliver trusted results
• Participate in team on call rotations
• Mentor junior engineers

What We’re Looking For 

• 2+ years of experience as a Data Engineer, preferably in a startup or fast-moving environment
• Advanced Proficiency with SQL, Python
• Solid understanding of data lakehouse concepts and design practices, data platform capabilities like transformation, orchestration, data governance (security and privacy controls, metadata management, data quality), data lineage, data observability, data processing and optimization techniques and data ops workflows
• Proficiency in modern data tech stack (cloud native data lakehouse like snowflake, fivetran/ stitch/ ELT pipelines, dbt, airflow/dagster/snowflake tasks and streams)
• Experience/ Familiarity with following tech is a big plus
• Streaming platforms like Kafka/ Flink, Event Driven Architectures
• BI tools like looker/Tableau 
• AWS Services like S3, lambda, kinesis, MSK, EC2, IAM, ECS 
• Desire to continually learn and keep up with advancements in data engineering practices
• Take ownership and ship medium to large features independently
• Excellent communication skills and able to regularly achieve consensus amongst teams
• A great team player who commits to the team and welcomes collaboration