Act as an advisor to leadership to develop or influence applications, network, information security, database, operating systems, or web technologies for highly complex business and technical needs across multiple groups.
Lead the strategy and resolution of highly complex and unique challenges requiring in-depth evaluation across multiple areas or the enterprise, delivering solutions that are long-term, large-scale and require vision, creativity, innovation, advanced analytical and inductive thinking.
Translate advanced technology experience, an in-depth knowledge of the organizations tactical and strategic business objectives, the enterprise technological environment, the organization structure, and strategic technological opportunities and requirements into technical engineering solutions.
Provide vision, direction and expertise to leadership on implementing innovative and significant business solutions.
Maintain knowledge of industry best practices and new technologies and recommends innovations that enhance operations or provide a competitive advantage to the organization.
Strategically engage with all levels of professionals and managers across the enterprise and serve as an expert advisor to leadership.
Design, develop, and maintain scalable ETL/ELT pipelines (batch and real-time/streaming) to ingest, process, enrich, and transform high-volume security, fraud, transaction, and operational data from diverse sources (SIEM, EDR, cloud logs, mainframe, payment networks, APIs, third-party threat feeds, etc.).
Build and optimize data models, data marts, and feature stores optimized for security analytics, fraud scoring, behavioral modeling, graph-based link analysis, and threat detection use cases.
Implement data quality, validation, reconciliation, and monitoring frameworks to ensure reliability of datasets used in production security & fraud systems.
Ensure data security & compliance throughout the pipeline — including encryption at rest/transit, data masking, access controls (RBAC/ABAC), lineage tracking, and audit logging — while meeting financial regulatory standards.
Collaborate with security data scientists and ML engineers to productionize features for anomaly detection, supervised/unsupervised fraud models, and cyber threat intelligence correlation.
Partner with platform & infrastructure teams to deploy and manage data infrastructure in cloud environments (AWS, Azure, GCP) using modern data stack tools.
Participate in on-call rotation to support critical production pipelines that power real-time fraud prevention and security monitoring.
Contribute to architecture decisions, technical roadmaps, and proof-of-concepts for emerging technologies in data-driven security (e.g., lakehouse architectures, zero-trust data access, federated querying).

Requirements

7+ years of Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education.
Strong proficiency in Python and/or Scala for data engineering tasks.
Hands-on experience with big data technologies: Spark (PySpark/Spark SQL), Kafka (or equivalent streaming), Airflow or similar orchestration.
Solid SQL skills and experience modeling complex analytical datasets (star/snowflake schemas, slowly changing dimensions, denormalized structures for analytics).
Experience with cloud data platforms (Snowflake, Databricks, BigQuery, Redshift, Delta Lake / Iceberg) and modern data lake/lakehouse architectures.
Familiarity with real-time/streaming data processing patterns and tools.
Understanding of data governance, data lineage, and security best practices in regulated industries.

Benefits

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Applicant Tracking System Keywords

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Hard Skills & Tools

PythonScalaSparkKafkaAirflowSQLSnowflakeDatabricksBigQueryRedshift

Soft Skills

leadershipstrategic thinkinganalytical thinkingcreativityinnovationcollaborationcommunicationproblem-solvingadvisory skillsvision