Data Engineer

TEKsystems•Richmond, VA

5d•$60 - $80•Remote

About The Position

Seeking a Data/AI Engineer to build and scale automated data pipelines across a diverse portfolio of client environments. This individual will connect messy, real-world financial and operational data from systems like ERPs, CRMs, and flat files into clean, structured cloud data platforms. The role is highly hands-on, focused on building reliable ELT pipelines, implementing data quality frameworks, and orchestrating workflows across multiple clients. A key component of the position is leveraging AI to automate traditionally manual data engineering tasks — including data cleaning, anomaly detection, and pipeline monitoring. This engineer will also play a critical role in onboarding new clients, standing up cloud data environments, and building reusable templates and frameworks to accelerate delivery.

Requirements

Strong hands-on experience building, deploying, and maintaining scalable data pipelines using Python and SQL in production environments.
Proven experience designing and implementing cloud-native data architectures (Snowflake, AWS preferred).
Background building automated ingestion pipelines and workflow orchestration for large datasets.
Exposure to transaction, ERP, or financial/accounting data structures.
Experience or interest in using AI tools (LLMs, coding assistants) to accelerate development, automate data quality, and optimize pipelines.
Advanced SQL + Python for end-to-end data pipeline engineering (building, transforming, and automating production pipelines).
Hands-on experience with modern data stacks (Snowflake/Databricks + ETL tools like Fivetran/Airbyte + orchestration tools like Airflow/Prefect).
AI/LLM integration in data workflows (using AI for data cleaning, anomaly detection, pipeline monitoring, and development acceleration).
Strong data quality and reliability mindset (handling messy, inconsistent source data and implementing automated validation/monitoring).
Ability to work independently and own projects end-to-end (single-threaded ownership of pipeline builds, client onboarding, and ongoing reliability across multiple environments).
ERP system experience (NetSuite, SAP, QuickBooks, Sage, Dynamics).
Financial / accounting data familiarity (GL, AR/AP, payroll, inventory, POS).
AI-powered pipelines (LLM classification, anomaly detection, entity resolution).
Consulting or multi-client services experience.
Exposure to private equity / transaction services environments.
Cloud fundamentals (AWS, Azure, GCP – storage, IAM, cost management).
Data security/compliance (SOC 2, GDPR).
3–7 years of data engineering experience in production environments.
Strong problem-solving ability with messy/unstructured datasets.
Ability to own pipeline reliability and data accuracy end-to-end.
Comfortable evaluating new tools and incorporating emerging AI capabilities.
Strong communication skills when working with client finance and IT teams.

Responsibilities

Build and scale automated data pipelines across a diverse portfolio of client environments.
Connect messy, real-world financial and operational data from systems like ERPs, CRMs, and flat files into clean, structured cloud data platforms.
Build reliable ELT pipelines.
Implement data quality frameworks.
Orchestrate workflows across multiple clients.
Leverage AI to automate traditionally manual data engineering tasks (data cleaning, anomaly detection, pipeline monitoring).
Onboard new clients.
Stand up cloud data environments.
Build reusable templates and frameworks to accelerate delivery.