Data Engineer

7Seventy; Northern (KY); Hybrid; USD 90,000 - 130,000; Full time

Job summary

7Seventy is seeking a Data Engineer to join a growing team focused on scalable data infrastructure for client projects. You will design and maintain pipelines, architect cloud data warehouses, and support analytics layers that drive decision-making.

The role blends hands-on execution with strategic thinking, requiring adaptability across tools and environments, and clear communication with technical and non-technical stakeholders. Remote work within the United States is supported.

Qualifications

• Bachelor's degree in CS, Engineering, Mathematics, or related field.
• 2–5 years of data engineering or software engineering experience.
• Advanced SQL skills with complex queries and stored procedures.
• Strong Python for pipeline scripting, automation, and data processing.
• Experience with dbt required; Spark a plus.
• Experience with ingestion tools (Fivetran, Airbyte, Rivery, Microsoft Fabric Data Factory).
• Experience with orchestration tools (Airflow, Prefect, Azure Data Factory, Microsoft Fabric).
• Experience with cloud platforms, Azure preferred, plus AWS or GCP.
• Experience with data warehouses (Snowflake, BigQuery, Redshift, Azure Synapse).
• Proficient with Git and code-review workflows.
• Strong communication and ability to explain technical concepts to non-technical stakeholders.

Responsibilities

• Design, build, and maintain scalable data pipelines for clients across industries.
• Architect and optimize cloud data warehouse solutions based on client stacks.
• Lead data integration projects from source systems through analytics layer.
• Work across modern data tools and platforms as client needs evolve.
• Collaborate with analysts and data scientists to ensure clean, reliable data.
• Champion data quality, testing, and observability across engagements.
• Produce technical documentation: pipeline architecture, data dictionaries, lineage, runbooks.
• Document engineering decisions and workflows for knowledge transfer.
• Research and evaluate emerging technologies; recommend tool investments.
• Train and mentor junior team members on engineering standards.
• Participate in client-facing requirements gathering and status updates.
• Provide support beyond core engineering, including Power BI dashboards and ad hoc reporting.

Skills

SQL

Python

dbt

Spark

Fivetran

Airbyte

Rivery

Microsoft Fabric

Airflow

Prefect

Azure Data Factory

Azure

AWS

GCP

Snowflake

BigQuery

Redshift

Azure Synapse

Git

Communication

Education

Bachelor's degree

Tools

dbt

Snowflake

BigQuery

Redshift

Azure Data Factory

Airflow

Power BI

Microsoft Fabric

Job description

This role is remote, and candidates must reside in the United States. For certain client projects, there may be occasional in-person requirements or limited travel to client offices.

About the Role

This opportunity is for a Data Engineer joining a growing team focused on helping clients build scalable, reliable data infrastructure. The role works across the modern data stack, supporting the design of pipelines, the architecture of data warehouses, and the development of analytical data layers that clients rely on for decision-making.

This is a high-impact, client-facing position that combines hands-on technical execution with strategic thinking. The role also requires adaptability across tools, platforms, and client environments, along with the ability to communicate clearly with both technical and non-technical stakeholders.

What You’ll Do

• Design, build, and maintain scalable data pipelines for clients across multiple industries.
• Architect and optimize cloud data warehouse solutions based on each client’s technology stack, which may include Snowflake, BigQuery, Redshift, Microsoft Fabric, or similar platforms.
• Lead data integration projects from source systems through the analytical layer, including scoping, delivery, and handoff.
• Work across a wide range of modern data tools and platforms as client needs evolve, learning new technologies quickly and applying best practices regardless of the toolset.
• Collaborate with analysts and data scientists to ensure data is clean, reliable, and well-modeled.
• Champion data quality, testing, and observability best practices across client engagements.
• Produce and maintain clear technical documentation, including pipeline architecture, data dictionaries, lineage maps, and runbooks, so clients can understand and manage their infrastructure over time.
• Document engineering decisions, standards, and workflows in a way that supports knowledge transfer to both clients and junior team members.
• Research and evaluate emerging technologies and recommend tooling investments that support the broader team.
• Train and mentor junior team members on engineering standards, pipeline design, and best practices.
• Participate in client-facing communication, including requirements gathering and project status updates.
• Provide support outside core engineering work when needed, including Power BI dashboard development, ad hoc reporting, and data visualization support.

Projects You May Work On

• ETL and ELT pipeline development and optimization.
• Data warehouse modeling, including dimensional and medallion/lakehouse architectures.
• Data integration across client systems such as CRM, ERP, marketing, and operational platforms.
• Infrastructure setup across the modern data stack, including ingestion, transformation, and orchestration layers.
• Implementations across platforms such as Microsoft Fabric, Databricks, and Snowflake.
• Data modeling and deployment across bronze, silver, and gold architecture layers.
• Data quality frameworks and automated pipeline testing.
• Cloud infrastructure provisioning and cost optimization across Azure, AWS, and GCP.
• Technical documentation projects, including data dictionaries, ER diagrams, lineage documentation, and metrics catalogs.
• Power BI semantic model development and dashboard support when business needs require it.

Qualifications

• Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field.
• 2 to 5 years of relevant experience in data engineering or software engineering.
• Advanced SQL skills, including complex query writing, query optimization, and stored procedures.
• Strong Python experience for pipeline scripting, automation, and data processing.
• Experience with dbt is required; Spark experience is a plus.
• Experience with ingestion tools such as Fivetran, Airbyte, Rivery, Microsoft Fabric Data Factory, or similar platforms.
• Experience with orchestration tools such as Airflow, Prefect, Azure Data Factory, Microsoft Fabric, or equivalent solutions.
• Experience with cloud platforms, with Azure preferred, along with AWS or GCP.
• Experience working with data warehouses such as Snowflake, BigQuery, Redshift, Microsoft Fabric, Azure Synapse, or equivalent platforms.
• Proficiency with Git, including branching strategies, pull requests, and code review workflows.
• Strong communication skills and the ability to explain technical concepts to non-technical stakeholders.
• Ability to work independently in a remote environment while managing multiple client workstreams.
• A player-coach mindset, with the ability to lead projects while helping junior teammates grow.
• Intellectual curiosity about evolving data tools, architecture patterns, and AI-augmented engineering workflows.

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