Lead Data Engineer

Analytical Mechanics Associates•Remote - California, CA

3d•$130,000 - $155,000•Hybrid

About The Position

Analytical Mechanics Associates (AMA) is seeking a full-time Lead Data Engineer with deep experience in data engineering, reporting, analytics, and end-to-end data platform development. The qualified candidate will join a hybrid project team (Central Time Zone) supporting dashboard development, data pipeline modernization, and governed analytics delivery in an agile environment.

Requirements

BA/BS in a technical field including but not limited to mathematics, information technology, engineering, accounting, or computational finance.
5 plus years of professional experience in business analytics or data engineering, with a minimum of 5 years of hands-on experience with BI visualization tools such as Power BI, Tableau, DOMO or equivalent platforms.
Strong production experience with Databricks, Snowflake, Azure Data Factory, or a comparable modern cloud data platform.
Advanced SQL and Python skills.
Experience building production ingestion, transformation, and analytics pipelines.
Experience implementing data governance, quality monitoring, lineage, and access controls.
Experience reverse-engineering and modernizing legacy SQL, SSIS, or integration workloads.
Experience with Git, Azure DevOps, CI/CD, testing, and controlled production releases.
Experience with Databricks, Snowflake, Azure Data Factory, or a comparable modern cloud data platform.
Experience with Data Vault 2.0 or another governed historical modeling approach.
Ability to communicate effectively with both technical teams and non-technical business stakeholders.
Demonstrated ownership of solutions from design through production support.
Strong documentation, troubleshooting, and problem-solving skills.
Must have a primary place of business in the Central Time Zone.

Nice To Haves

Certification in data visualization with Power BI.
Experience with Microsoft Fabric Lakehouse, Warehouse, Data Pipelines, Dataflow Gen2, DirectLake, and semantic models.
Experience with Spark, Delta Lake, dbt, or dbt-fabric.
Proficiency in DAX, Tableau, or other BI visualization tools.
Experience with API integrations, vendor-file automation, and workflow automation.
Experience with AWS-based data services (Lambda, S3, Glue, Athena).
Familiarity with Azure Key Vault, service principals, managed identities, and Microsoft Purview.
Experience setting technical standards and mentoring engineers or analysts.
Master's degree in Data Science, Applied Mathematics, or a related field.

Responsibilities

Brainstorm and lead analytics product design including dashboard layouts, visualization approaches, and semantic model architecture.
Design and develop production-grade reports and dashboards, including direct stakeholder engagement and KPI validation.
Build certified Power BI semantic models, executive dashboards, operational reporting, and governed self-service analytics.
Design and implement data pipelines using tools such as Databricks, Azure Data Factory, or Snowflake, following a structured layered architecture (Raw, Transformed, Analytics).
Implement automated data quality checks, reconciliation, pipeline monitoring, alerts, and operational support dashboards.
Reverse-engineer and modernize legacy SQL, SSIS, or integration workloads into maintainable modern patterns.
Establish and enforce engineering standards for SQL, Python, notebooks, repositories, testing, documentation, and naming conventions.
Implement Azure DevOps branching strategies, CI/CD pipelines, release management, and environment separation.
Design enterprise data governance frameworks including column-level documentation, role-based access controls, standardized metric definitions, and full data lineage traceability.
Partner with business leaders and stakeholders to translate operational and analytical needs into scalable technical solutions.
Assist in the technical direction of the overall project team covering two primary work streams: data environment and data presentation.
Mentor team members and help establish a consistent, collaborative engineering culture.
Help troubleshoot and overcome technical issues, proactively detecting data issues through automated validation and monitoring.