Lead Data Engineer - Flexible, Real-Time Pipelines

Quest Oracle Community; Northern (KY); Hybrid; USD 125,000 - 182,000; Full time

Benefits offered by this job

Health insurance

401(k) plan with company match

Tuition reimbursement

Paid time off and holidays

Volunteer time

Job summary

Empower is seeking a Lead Data Engineer to architect and optimize scalable data pipelines, real-time ingestion, and data platforms. You will mentor engineers, guide technical decisions, and ensure data governance and security across enterprise systems.

You will design event-driven architectures, implement schema evolution, and integrate AI-assisted tooling to enhance pipeline reliability and efficiency globally.

Qualifications

• Bachelor’s degree or equivalent combination of training and experience.
• 6–8+ years of data engineering experience with enterprise scale platforms.
• Strong experience with Apache Kafka, Confluent Cloud, AWS Kinesis, or Apache Pulsar.
• Advanced proficiency in Python, PySpark, and SQL.

Responsibilities

• Lead design and implementation of advanced data pipelines and real-time streaming solutions.
• Build and maintain scalable Kafka producers/consumers for sub-second latency.
• Configure CDC solutions (Debezium, AWS DMS, Qlik Replicate) across sources to Kafka.
• Develop stream-processing logic with Flink, Spark Streaming, Kafka Streams, or dbt-mesh.

Skills

Kafka

Python

Java

Go

SQL

PySpark

Flink

Spark Streaming

Kubernetes

Education

Bachelor’s degree in computer science or related field

Equivalent training/experience

Tools

Confluent Cloud

AWS Kinesis

Debezium

Kafka Connect

PostgreSQL

MySQL

MongoDB

Oracle

Iceberg/Delta Lake

Snowflake/BigQuery

Docker

Kubernetes

Terraform

CI/CD

Job description

Empower is seeking a Lead Data Engineer to architect and optimize scalable data pipelines, real-time ingestion, and data platforms. You will mentor engineers, guide technical decisions, and ensure data governance and security across enterprise systems.

You will design event-driven architectures, implement schema evolution, and integrate AI-assisted tooling to enhance pipeline reliability and efficiency globally.