Data Engineer
5 days ago
We are seeking a highly skilled and results-driven Data Engineer with proven expertise in data engineering, reporting, and analytics, specializing in Palantir Foundry. The ideal candidate will have experience designing scalable data architectures, building robust pipelines, and enabling actionable insights through optimized data solutions.
This role requires hands-on technical excellence, a strong understanding of big data ecosystems, and the ability to work closely with cross-functional teams to translate business requirements into efficient technical implementations.
Key Responsibilities
- Design, develop, and maintain end-to-end data pipelines using Palantir Foundry, including the code repository, Contour for data analysis, health checks, and data expectations.
- Build and optimize PySpark-based ETL pipelines for large-scale structured and unstructured data processing.
- Manage data ingestion, transformation, and quality monitoring for high volume, high-velocity datasets.
- Design scalable and efficient data models aligning with business requirements and analytics needs.
- Implement data warehousing solutions and manage data lake / lakehouse architecture for analytics.
- Optimize Spark and PySpark pipelines for cost efficiency, scalability, and low latency.
- Leverage Hadoop/Spark ecosystems to handle distributed data processing effectively.
- Collaborate with analytics and BI teams to enable self-service analytics and robust reporting capabilities.
- Support financial reporting and valuations (e.g., IFRS17) where applicable.
- Define and implement data validation, profiling, and lineage tracking within Palantir Foundry.
- Establish best practices for data security, privacy, and compliance.
- Work closely with data scientists, analysts, and business teams to deliver actionable insights.
- Translate complex business problems into scalable, technical data solutions.
- 3+ years of hands-on experience in data engineering and big data ecosystems.
- Expertise in Palantir Foundry (pipelines, Contour, health checks, expectations).
- Strong programming skills in PySpark, Python, and SQL for data manipulation and analysis.
- Solid understanding of Hadoop, Spark, Kafka, and distributed data systems.
- Experience in data warehousing concepts (Snowflake, Redshift, BigQuery, or similar) is a plus.
- Exposure to cloud platforms (AWS, GCP, or Azure) and their big data services (Glue, EMR, Dataproc, Databricks).
- Experience in CI/CD for data pipelines (Git, Jenkins, or equivalent).
- Understanding of containerization & orchestration (Docker, Kubernetes, Airflow, or Dagster).
- Knowledge of real-time data streaming frameworks (Kafka, Flink, or Kinesis).
- Familiarity with data cataloging & governance tools (Alation, Collibra).
- Familiarity with TypeScript, HTML, CSS, JavaScript, and frameworks like React, Angular, , or
- Experience with modern visualization tools (Tableau, Power BI, Looker, or Superset).
- Exposure to financial services, risk modeling, or IFRS17 reporting is a strong plus.
Academic Background
- Bachelors or Masters degree in Mathematics, Statistics, Operations Research, Industrial Engineering, Computer Science, Econometrics, or Information Technology.
Soft Skills
- Strong analytical and problem-solving skills.
- Excellent communication and stakeholder management abilities.
- Ability to work in agile, cross-functional teams with a high degree of ownership.
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