Senior Data Engineer

Position: Senior Data Engineer

Location: Louisville, Kentucky (Remote, Hybrid)

FLSA Status: Exempt

Job Summary

The Senior Data Engineer is responsible for designing, building, optimizing, and supporting reliable data pipelines and cloud data platform services that enable enterprise analytics, reporting, operational insights, and data-driven decision-making. This role focuses on the engineering foundations that move, transform, govern, and deliver trusted data across eBlu s contemporary data ecosystem.

This position requires strong hands-on experience with Azure SQL, Google Cloud Platform, BigQuery, Airbyte, Airflow, dbt, SQL, and modern data pipeline patterns. The Senior Data Engineer partners closely with analytics engineers, data analysts, software engineers, product stakeholders, and business leaders to ensure data is accurate, timely, scalable, secure, and accessible for analytical and operational use cases.

The role balances pipeline engineering execution, platform reliability, data architecture, data quality, cost optimization, documentation, and technical mentorship while helping modernize eBlu s data platform and improve confidence in enterprise data assets.

Key Responsibilities

Modern Data Pipeline Engineering

- Design, build, test, deploy, and maintain scalable batch and scheduled data pipelines using Airbyte, Airflow, dbt, SQL, BigQuery, Azure SQL, and cloud-native data services.

- Engineer reliable ingestion, orchestration, transformation, validation, monitoring, alerting, and recovery patterns for ELT and ETL workflows.

- Build and maintain source system integrations, extraction jobs, landing patterns, staging structures, incremental loads, and change data capture approaches where appropriate.

- Develop reusable pipeline frameworks, templates, and engineering patterns that improve delivery speed, maintainability, and operational consistency.

- Ensure pipelines are designed for performance, resiliency, observability, scalability, cost efficiency, and ease of support.

Cloud Data Platform Warehouse Development





- Design, implement, and optimize cloud data structures across Google Cloud Platform, BigQuery, Azure SQL, and related data services.

- Develop and maintain raw, staged, curated, and consumption-ready data layers that support analytics engineering, business intelligence, reporting, and downstream operational workflows.

- Partner with architecture and engineering leaders to improve data platform cohesion across application, integration, and data layers.

- Optimize BigQuery datasets, partitioning, clustering, indexing, query performance, storage patterns, and workload costs.

- Contribute to modernization roadmaps that reduce data platform technical debt and improve scalability, maintainability, and governance.

Data Transformation, Quality Governance

- Build and support transformation workflows in dbt and SQL, including models, tests, snapshots, macros, documentation, lineage, and deployment practices.

- Implement data quality checks, reconciliation logic, anomaly detection patterns, data freshness monitoring, and source-to-target validation.

- Partner with analytics engineers and stakeholders to ensure curated datasets are accurate, well-documented, trusted, and aligned to business definitions.

- Promote strong data governance practices, including ownership, metadata, lineage, access controls, naming standards, documentation, and data retention awareness.

- Support root cause analysis and remediation for data incidents, pipeline failures, quality issues, and performance bottlenecks.

DevOps, Automation Operational Excellence

- Apply modern engineering practices such as version control, peer review, automated testing, CI/CD,



environment management, release discipline, and repeatable deployment processes for data assets.

- Create and maintain operational dashboards, job health monitoring, runbooks, failure response procedures, and pipeline support documentation.

- Improve reliability through proactive monitoring, alerting, backfill strategies, retry logic, dependency management, and incident response practices.

- Collaborate with DevOps, platform, security, and engineering teams to strengthen cloud utilization, access management, observability, resiliency, and compliance alignment.

- Evaluate and recommend tools, patterns, and automations that improve productivity, reliability, security, and cost efficiency across the data platform.

Cross-Functional Partnership Technical Leadership

- Work closely with analytics engineers, product managers, engineering teams, and business stakeholders to understand data needs and deliver reliable data solutions.

- Translate business and technical requirements into scalable ingestion, transformation, storage, and delivery designs.

- Provide senior-level technical guidance, code review, troubleshooting support, and mentorship to data and analytics team members.

- Communicate technical concepts, tradeoffs, risks, and delivery status clearly to both technical and non-technical audiences.

- Foster a culture of ownership, documentation, operational discipline, continuous improvement, and responsible innovation.

Experience, Skills Qualifications

Education

- Bachelor s degree in Computer Science, Information Systems, Data Engineering, Engineering, Data Science, or a related technical field required.

- Master s degree or equivalent experience preferred.

Experience

- 5+ years of progressive

Disclaimer : This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

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