Senior Data Analytics Engineer

Posted 6 months agoViewed

💎 Seniority level: Senior, Minimum of 10 years

📍 Location: United States

🗣️ Languages: English

⏳ Experience: Minimum of 10 years

🪄 Skills: AWSPythonSQLBashData AnalysisElasticSearchETLGitKafkaKibanaKubernetesMySQLApache KafkaGoCollaborationCI/CDLinux

Bachelor's or Master's degree in Computer Science, Information Technology, or a related field.
Minimum of 10 years of experience in ETL operations, Systems Operations, and Data Analytics.
Expert knowledge of SQL, git, various data formats (JSON, YAML, CSV), and MS Excel.
Expert Python and Bash skills including OO techniques.
Proficiency in Ruby, Go, and other languages is a plus.
Familiarity with Argo CD/Workflow, Kubernetes (K8s), containers, GitHub actions, Linux, and AWS is highly desirable.
Strong problem-solving skills and attention to detail.
Excellent communication and collaboration skills.
Ability to work independently and as part of a team.
Strong proficiency in SQL and experience with MySQL or similar relational databases.
Must be able to interact with databases using raw-SQL.
Solid understanding of data modeling concepts and techniques.
Experience with Jaspersoft or similar reporting tools is preferred.

Design, develop, and maintain ETL processes to extract, transform, and load data from various sources.
Monitor and optimize ETL workflows to ensure data quality and performance.
Collaborate with cross-functional teams to gather and understand data requirements.
Create and maintain documentation for ETL processes and data analytics solutions.
Create and maintain data models to support reporting and analysis needs.
Support and troubleshoot ETL processes and resolve any issues in a timely manner.
Perform data analysis, develop dashboards, and present actionable insights to stakeholders.

Posted 23 days ago

📍 United States, United Kingdom

🧭 Full-Time

💸 175000.0 - 191300.0 USD per year

🔍 Software Development

🔧 Requirements

8+ years of experience in data engineering, analytics engineering, or related fields.
Strong experience with cloud-based data warehouses (Redshift, Snowflake, or BigQuery) and query performance optimization.
Expertise in SQL, Python, and data transformation frameworks like dbt.
Experience building scalable data pipelines with modern orchestration tools (Airflow, MWAA, Dagster, etc.).
Knowledge of real-time streaming architectures (Kafka, Kinesis, etc.) and event-based telemetry best practices.
Experience working with business intelligence tools (e.g. Looker) and enabling self-serve analytics.
Ability to drive cost-efficient and scalable data solutions, balancing performance with resource management.
Familiarity with machine learning operations (MLOps) and experimentation tooling is a plus.
Strong problem-solving and communication skills—comfortable working cross-functionally with technical and non-technical stakeholders.

💡 Responsibilities

Develop, own and improve Kickstarter’s data architecture—optimize our Redshift warehouse, implement best practices for data storage, processing, and orchestration.
Design and build scalable ETL/ELT pipelines to transform raw data into clean, usable datasets for analytics, product insights, and machine learning applications.
Enhance data accessibility and self-service analytics by improving Looker models and enabling better organizational data literacy.
Support real-time data needs by optimizing event-based telemetry and integrating new data streams to fuel new products, personalization, recommendations, and fraud detection.
Lead cost optimization efforts—identify and implement more efficient processes and tools to lower costs.
Drive data governance and security best practices—ensure data integrity, access controls, and proper lineage tracking.
Collaborate across teams to ensure data solutions align with product, growth, and business intelligence needs.

PythonSQLETLKafkaSnowflakeAirflowData engineeringData visualizationData modeling

Posted 23 days ago