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Tech Stack
Tools & technologiesAirflowAmazon RedshiftApacheBigQueryCloudETLHadoopJavaKafkaNoSQLPythonScalaSparkSQL
About the role
Key responsibilities & impact- Lead the data architecture, design, and deployment of scalable, high-throughput Big Data systems into production environments.
- Architect, deploy, and manage the foundational data systems that underlie modern AI infrastructure, including vector, NoSQL, and document databases.
- Develop end-to-end data engineering solutions, including robust ETL/ELT pipelines, API services, and data ingestion frameworks.
- Design and build the storage and processing layers powering our analytics workloads: data lakes, data warehouses, distributed file systems, and real-time streaming architectures.
- Engineer feature-rich context pipelines that process large-scale enterprise data, balancing batch and streaming patterns seamlessly.
- Optimize and scale large distributed queries and data transformations to ensure high performance and low latency for end users.
- Implement data quality frameworks to measure and ensure data integrity, reliability, and governance across all data assets.
- Collaborate with analytics, product, and platform teams to build data models that capture the semantics of customer metrics, hierarchies, and relationships.
- Stay current with the modern data stack and big data landscape, evaluating new tools, distributed computing frameworks, and database technologies for potential adoption.
Requirements
What you’ll need- 7+ years of dedicated data engineering experience, demonstrating a strong track record of hands-on execution and delivery in complex data environments.
- Deep practical understanding of the database ecosystems that power AI and machine learning infrastructure (e.g., Vector databases, NoSQL, and Document stores).
- Hands-on experience building, scaling, and shipping large-scale data platforms in production.
- Deep practical experience with distributed data processing frameworks (e.g., Apache Spark, Flink, Hadoop).
- Strong expertise in message brokers and event streaming platforms (e.g., Apache Kafka, Kinesis).
- End-to-end exposure to data pipeline lifecycle development, including extensive experience with workflow orchestration tools (e.g., Apache Airflow, Dagster).
- Hands-on expertise with cloud data warehouses (e.g., Snowflake, BigQuery, Redshift) and data lake architectures (e.g., Databricks, Delta Lake, Apache Iceberg).
- Advanced SQL skills and proficiency in Python, Scala, or Java.
- Strong background in modern software development practices (testing, code review, CI/CD, Infrastructure as Code).
Benefits
Comp & perks- Our Winning Culture is the engine that drives our teams of innovators.
- We champion diversity of thought and ideas.
- We behave like leaders regardless of title.
- We are committed to achieving ambitious goals.
- We love celebrating our wins – big and small.
- DEIB improves our workforce, enhances trust with our partners and customers, and drives business success.
- We hire you for who you are, and we want you to bring your authentic self to work every day!
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
data architectureETLELTdata engineeringdistributed data processingSQLPythonScalaJavadata quality frameworks
Soft Skills
collaborationcommunicationproblem-solvingleadershiporganizational skills