Data Engineer: AI Pipelines & Enterprise Data

Akiva Ai, Llc; Northern (KY); Hybrid; USD 120,000 - 150,000; Full time

Benefits offered by this job

401(k) with generous company match

Medical Insurance

Vision Insurance

Short-Term Disability Insurance

Long-Term Disability Insurance

Paid Time Off

Profit Sharing

Job summary

AKIVA is a data engineering firm seeking a candidate to design and build the data infrastructure powering AKIVA AI systems. You will architect pipelines that move terabytes reliably, enable ML at scale, and meet enterprise governance requirements.

You will build batch and streaming data pipelines, design data lake/warehouse schemas, implement data quality and lineage controls, and collaborate with ML engineers on feature retrieval and cost/performance optimization of cloud data platforms.

Qualifications

• 3+ years in data engineering on modern stacks.
• Strong SQL and Python; experience with at least one orchestrator (Airflow, Dagster, Prefect).
• Production experience with cloud data warehouses (Snowflake, BigQuery, Redshift) or lakehouses (Databricks, Iceberg).
• Solid understanding of data modeling and pipeline reliability.

Responsibilities

• Build batch and streaming data pipelines that feed training, evaluation, and inference systems.
• Design schemas and storage layers (data lake, warehouse, vector stores) for ML workloads.
• Implement data quality, lineage, and governance controls.
• Partner with ML engineers on feature engineering and retrieval pipelines.
• Optimize cost and performance of cloud data platforms.

Skills

Data engineering

SQL

Python

Orchestrators

Tools

Airflow

Dagster

Prefect

Snowflake

BigQuery

Redshift

Databricks

Iceberg

Pinecone

Weaviate

pgvector

Qdrant

Kafka

Kinesis

Pulsar

Job description

AKIVA is a data engineering firm seeking a candidate to design and build the data infrastructure powering AKIVA AI systems. You will architect pipelines that move terabytes reliably, enable ML at scale, and meet enterprise governance requirements.

You will build batch and streaming data pipelines, design data lake/warehouse schemas, implement data quality and lineage controls, and collaborate with ML engineers on feature retrieval and cost/performance optimization of cloud data platforms.