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Lead Data Analytics Engineer Jobs in Washington (NOW HIRING)

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Lead Data Analytics Engineer information

What does a Lead Data Analytics Engineer do?

A Lead Data Analytics Engineer oversees the design, development, and maintenance of data analytics systems within an organization. They lead teams to build data pipelines, optimize data workflows, and ensure data quality and accessibility for business insights. Their role often involves collaborating with data scientists, analysts, and stakeholders to translate business requirements into technical solutions. Additionally, they are responsible for setting best practices, mentoring team members, and staying updated with emerging technologies in data engineering.

What are the key skills and qualifications needed to thrive as a Lead Data Analytics Engineer?

To thrive as a Lead Data Analytics Engineer, you need advanced expertise in data modeling, statistical analysis, and programming, typically supported by a degree in computer science, statistics, or a related field. Mastery of tools such as SQL, Python, R, cloud platforms (like AWS or Azure), and data visualization software, along with certifications like AWS Certified Data Analytics or Google Professional Data Engineer, is highly valued. Strong leadership, problem-solving, and communication skills help you guide teams and translate complex data insights to stakeholders. These competencies are essential for delivering impactful analytics solutions and driving data-driven decision-making within organizations.

How does a Lead Data Analytics Engineer typically collaborate with cross-functional teams?

A Lead Data Analytics Engineer frequently partners with data scientists, business analysts, and software engineers to design and implement scalable analytics solutions. They often act as a bridge between technical teams and business stakeholders, translating business requirements into actionable data models and pipelines. Effective communication and project management skills are crucial in ensuring alignment on goals, timelines, and deliverables. Regular meetings and agile workflows are common, fostering a collaborative environment that supports innovation and timely project delivery.

What is the difference between Lead Data Analytics Engineer vs Data Scientist?

AspectLead Data Analytics EngineerData Scientist
CredentialsBachelor's or Master's in Data Science, Computer Science, or related fields; certifications like AWS, Azure, or Google CloudBachelor's or Master's in Data Science, Statistics, or related fields; similar certifications
Work EnvironmentFocus on data infrastructure, pipelines, and analytics tools; often in engineering teamsFocus on statistical modeling, machine learning, and data interpretation; often in research or analytics teams
Employer & Industry UsageUsed in tech, finance, healthcare for building data systems and analytics platformsUsed across industries for predictive modeling, research, and insights generation

The main difference is that Lead Data Analytics Engineers primarily focus on building and maintaining data infrastructure and analytics pipelines, while Data Scientists concentrate on analyzing data, creating models, and deriving insights. Both roles require strong technical skills and often overlap, but their core responsibilities differ in scope and focus.

What cities in Washington are hiring for Lead Data Analytics Engineer jobs?

Cities in Washington with the most Lead Data Analytics Engineer job openings:

Infographic showing various Lead Data Analytics Engineer job openings in Washington as of August 2026, with employment types broken down into 85% Full Time, and 15% Contract. Highlights an 71% In-person, 2% Hybrid, and 27% Remote job distribution.

Lead AI & Data Analytics Engineer (Palantir) with Security Clearance

Arlington, VA • On-site

Anonymous Employer
See more jobs at Anonymous Employerexternal

$118K - $155K/yr

Other

Posted 3 days ago

New

Job description

This is a full-time opportunity with hybrid flexibility in the D.C. metro area. We are seeking a proactive Lead AI & Data Analytics Engineer to support a federal acquisition process modernization effort. This individual will serve as the technical and functional lead for building AI-enabled analytics products using Palantir Foundry, Microsoft Power Platform, Advana, Jupiter, and Databricks. This is not a traditional dashboard developer role. The successful candidate must be a self-starter who can engage directly with business stakeholders, understand acquisition and contract writing processes, identify high-value use cases, connect data across platforms, and deliver decision-support capabilities with limited oversight. Required Qualifications: - 5+ years of experience in data analytics, data engineering, platform engineering, business intelligence, AI/ML, or analytics product development.
- Experience building analytics products in Palantir Foundry.
- Experience working with Advana, Jupiter, or similar federal/DoD data environments.
- Strong experience with SQL, Python, TypeScript/JavaScript.
- Hands-on expertise with one or more modern data engineering, analytics, or data platform technologies (e.g. Databricks, Spark, Kafka, Hadoop, Snowflake, Azure Data Factory, AWS Glue, Apache NiFi, Delta Lake).
- Experience integrating and transforming data from multiple sources and translating business requirements into data products, dashboards, AI use cases, and technical solutions.
- Experience applying AI, machine learning, NLP, generative AI, semantic search, or natural language querying to business or operational data.
- Strong understanding of data governance, data quality, access control, and responsible AI practices.
- Demonstrated ability to lead use cases, manage ambiguity, and deliver results with limited oversight.
- Strong client-facing communication skills and stakeholder engagement skills.
- Eligible to obtain a U.S. Government clearance
- Experience with Microsoft Power Platform, including Power BI; Power Apps and Power Automate.

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