Data Engineer

We’re looking for an experienced Data Engineer to design, build, and maintain scalable data platforms and pipelines that power analytics, reporting, machine learning, and AI applications.

This is a hands‑on engineering role for someone who enjoys solving complex data problems, working with large datasets, and building reliable systems that make data accessible and useful across the organization.

What You’ll Do

Design, build, and maintain scalable ETL/ELT data pipelines

Develop data ingestion and transformation processes across structured, semi-structured, and unstructured data sources

Build and optimize data warehouses, data lakes, and lakehouse architectures

Develop reliable batch and real‑time/streaming data pipelines

Design data models that support analytics, reporting, operational applications, and AI/ML use cases

Integrate data from APIs, databases, SaaS platforms, files, and third‑party systems

Improve data quality, reliability, observability, and performance

Build automated processes for data validation, testing, monitoring, and error handling

Optimize queries, storage, compute, and pipeline performance

Implement appropriate data security, governance, access controls, and privacy standards

Partner with data scientists, AI/ML engineers, analysts, software engineers, and business stakeholders

Support data infrastructure through CI/CD, infrastructure automation, and modern DataOps practices

Troubleshoot complex production data issues and identify opportunities to improve platform scalability and reliability

What We’re Looking For

4+ years of professional Data Engineering or related experience

Strong proficiency with SQL

Strong programming skills in Python

Experience designing and building production‑grade ETL/ELT pipelines

Experience with modern cloud data platforms such as Snowflake, Databricks, BigQuery, Redshift, or Microsoft Fabric

Hands‑on experience with AWS, Azure, or GCP

Experience with data transformation and orchestration technologies such as dbt, Airflow, Dagster, Prefect, or similar

Experience working with relational and NoSQL databases

Understanding of dimensional modeling, data warehousing, lakehouse architectures, and distributed data processing

Experience with technologies such as Spark, Kafka, or similar distributed/streaming platforms

Familiarity with Git, CI/CD, Docker, and modern software engineering practices

Strong understanding of data quality, lineage, governance, security, and observability

Ability to translate business and technical requirements into scalable data solutions

Nice to Have

Experience building data infrastructure for AI/ML and Generative AI applications

Experience with vector databases, embeddings, or unstructured data pipelines

Experience with real‑time event‑driven architectures

Experience with Terraform or other Infrastructure‑as‑Code tools

Experience with Kubernetes

Experience implementing data catalogs, lineage, and governance platforms

Experience supporting high‑volume or highly distributed data environments

What Success Looks Like

You build data systems people can trust. Your pipelines are reliable, scalable, observable, and designed with downstream users in mind. You understand that strong data engineering isn't just about moving data from one place to another—it's about creating a foundation that allows analytics, applications, and AI systems to operate effectively at scale.

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