Staff Data Engineer

Posted 15 days agoViewed

💎 Seniority level: Staff, 7+ years

📍 Location: AL, AR, AZ, CA, CO, CT, FL, GA, ID, IL, IN, IA, KS, KY, MA, ME, MD, MI, MN, MO, MT, NC, NE, NJ, NM, NV, NY, OH, OK, OR, PA, SC, SD, TN, TX, UT, VT, VA, WA, WI

🔍 Industry: Insurance

🏢 Company: Kin Insurance

🗣️ Languages: English

⏳ Experience: 7+ years

🪄 Skills: AWSPythonSQLCloud ComputingETLKafkaData engineeringData StructuresPandasMentoringTime ManagementExcellent communication skillsData visualizationData modelingData management

Requirements:

7+ years of hands-on data engineering experience related to: Data structures and cloud platform environments and best practices (AWS strongly preferred, Azure, or GCP)
ETL performance tuning and cost optimization
Data lake and lakehouse patterns including open table formats (e.g. Iceberg, Hudi, Delta)
Proficiency in Python (Pandas, NumPy, etc.) and SQL for advanced data processing and querying
Experience influencing technical strategy, optimizing systems for scale, and reviewing team designs
Expertise in distributed data processing/storage (e.g., Apache Spark, Kafka, Hadoop, or similar)
Excellent communication skills and ability to explain complex concepts clearly and concisely
Detail-oriented with strong data intuition and a passion for data quality
Proven ability to model data and build production-ready ETL pipelines handling TBs of data.
Great time management and prioritization skills

Responsibilities:

Designing and developing scalable data pipelines and models for downstream analytics and reporting
Leading and collaborating with a cross-functional project team to implement data validation, QA standards, and effective data lifecycle management
Optimizing pipeline performance, cost, and data quality in a large-scale data environment
Migrating data warehouse (DBT, Redshift) architecture to Lakehouse (e.g: S3, Glue, Databricks, Unity catalog) architecture
Mentoring data engineers and promoting best practices in software engineering, documentation, and metadata management
Ensuring data security and compliance with regulations (e.g., GDPR, CCPA, GLBA) through robust pipeline design and access monitoring
Translating ambiguous business requirements into technical solutions using marketing domain knowledge

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