Lead Data Engineer - Platform & Analytics

Analytical Mechanics Associates, Inc.

United States

Hybrid

USD 130,000 - 155,000

Full time

8 days ago

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Benefits offered by this job

Competitive salary

401(k) matching

Tuition reimbursement

Short/long-term disability

Medical, dental, and vision insurance

Job summary

Analytical Mechanics Associates, Inc. is seeking a Lead Data Engineer to drive end-to-end data platform development and analytics governance. This hybrid role sits in the Central Time Zone and partners with stakeholders to deliver scalable data solutions and dashboards.

You will mentor engineers, implement robust data pipelines with Databricks, Snowflake, and ADF, and enforce coding standards across SQL and Python. Strong communication with technical and business teams is essential.

Qualifications

BA/BS in a technical field including mathematics, information technology, engineering, accounting, or computational finance.
5+ years in business analytics or data engineering with hands-on BI tools (Power BI, Tableau, DOMO or equivalent).
Strong production experience with Databricks, Snowflake, ADF or similar 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 with Databricks, Snowflake, ADF or similar cloud data platform.
Experience with Data Vault 2.0 or other governed historical modeling.
Ability to communicate with technical and non-technical stakeholders.
Ownership of solutions from design to production support.
Strong documentation, troubleshooting, and problem-solving skills.
Must have a primary place of business in the Central Time Zone.

Responsibilities

Brainstorm and lead analytics product design including dashboards and semantic model architecture.
Design and develop production-grade reports and dashboards with KPI validation.
Build certified Power BI semantic models, executive dashboards, and governance analytics.
Design and implement data pipelines using Databricks, ADF, or Snowflake with layered architecture.
Implement automated data quality checks, reconciliation, and monitoring dashboards.
Reverse-engineer and modernize legacy SQL, SSIS, or integration workloads.
Establish engineering standards for SQL, Python, notebooks, repositories, and testing.
Implement Azure DevOps branching, CI/CD pipelines, and release management.
Develop data governance frameworks with documentation and full data lineage traceability.
Partner with business leaders to translate needs into scalable technical solutions.
Contribute to two primary streams: data environment and data presentation.
Mentor team members and foster a collaborative engineering culture.
Troubleshoot issues and detect data problems through automated validation.