Lead and manage multiple scrum teams (data engineering and platform integrations), setting goals, collaborating closely with Product and QE peers, and coaching engineering excellence.
Architect, design, and deliver production ready data pipelines (batch and streaming) and data models that consolidate core sources into reliable, reusable datasets.
Develop and optimize ETL/ELT using SQL and Python ; author, review, and tune stored procedures and queries on primary SQL platforms.
Apply AI assisted development to accelerate coding, generate tests, improve documentation, and detect anomalies; embed automated quality checks and observability.
Define integration patterns and standards; oversee API development and platform integration practices across services and partners.
Govern and enhance the iPaaS footprint (e.g., Boomi) and EDI flows; ensure reliability, traceability, and compliance with operational controls.
Establish and manage the external API gateway capability with appropriate authentication, authorization, rate limiting, logging, and lifecycle management.
Implement orchestration, version control, and CI/CD for data and integration of assets; promote reusable templates, code conventions, and infrastructure as code.
Define data contracts, lineage, and schema evolution practices; enforce SLAs and SLOs for data product availability, freshness, and quality.
Drive performance tuning on SQL workloads and cost-efficient storage strategies; monitor reliability and capacity across environments.
Partner with Security and Governance to enforce access controls , encryption, privacy, and auditability; remediate findings proactively.
Lead root cause analysis of complex incidents; prioritize and retire technical debt , improve resiliency, and prevent recurrence through design changes.
Collaborate with Product and business stakeholders to translate requirements into scalable solutions, shared metrics, and well documented interfaces.
Provide technical reviews and mentoring; align database design, pipelines, APIs, and platform choices with Navia’s architecture standards.
Other duties as assigned.

Requirements

Bachelor’s degree in Computer Science, Information Systems, or related field, or equivalent experience (preferred).
6-8 + years in data engineering or closely related roles, delivering and operating pipelines for large, heterogeneous datasets.
Demonstrated ability to lead technical teams and set engineering standards.
Expert level SQL across major RDBMS (e.g., SQL Server, Oracle, MySQL); advanced query and stored procedure optimization.
Proficiency in Python for data engineering (e.g., pandas, PySpark); familiarity with Java/Scala is a plus.
Hands-on experience with modern platforms (e.g., Databricks , Snowflake ) and Iceberg compatible table formats.
Knowledge of orchestration and transformation tooling (e.g., Airflow , Dagster , dbt ); CI/CD for data and integration of assets.
Understanding of streaming technologies ( Kafka , Spark Structured Streaming ) and Change Data Capture (CDC) practices.
Experience with API design , external API gateway administration, and secure partner integrations; familiarity with iPaaS (e.g., Boomi ) and EDI .
Strong grasp of data modeling, metadata, lineage, governance, and compliance; proven ability to set and enforce standards.
Comfort with Git-based workflows, containers, infrastructure as code (e.g., Terraform), and cloud services; focus on reliability and cost control.
Demonstrated AI first approach to engineering—practical use of LLMs and AI tooling to enhance development, testing, documentation, and data quality.
Experience working in agile teams; effective communication with technical and executive stakeholders.
Industry experience in financial services is a plus.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard skills

data engineeringETLELTSQLPythonAPI developmentdata modelingorchestrationstreaming technologiesAI assisted development

Soft skills

leadershipcollaborationcoachingtechnical reviewsmentoringcommunicationproblem-solvingroot cause analysisprioritizationstakeholder engagement