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Lead Analytics Engineer Jobs

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Embedded Software Engineer II, Robotic Controls
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United States , Austin
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Performance Analyst
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United States , Houston
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EDP
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Implementation Project Manager
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United States , Chicago
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109400.00 - 130613.00 USD / Year
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Intercom
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Head of Product
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Germany , Berlin
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Adevinta
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Systems Engineer (Presales) - Enterprise, West India
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India , Mumbai
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Palo Alto Networks
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Senior Advisor - Business Transformation
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United Kingdom , Coventry
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60000.00 - 70000.00 GBP / Year
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Manufacturing Technology Centre
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Retail Territory Manager
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El Salvador , San Salvador
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Puma Energy
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Senior Web Developer
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India , Bangalore
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Position2
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Senior Director, Partnerships
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United States , New York
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Chat3D
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UK CRM - Design Lead Vice President
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India , Pune
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Barclays
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Account/Delivery Manager
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United States
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LeverX
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Business Data Analyst - Capital Markets
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Canada , Mississauga
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111600.00 - 161600.00 USD / Year
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Citi
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Senior AWS Data Engineer / Data Platform Engineer
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United Arab Emirates , Dubai
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NorthBay
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Director, Research Programs
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United States , Lebanon, NH
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113700.00 - 142200.00 USD / Year
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Beatrice Daily Sun
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Staff Software Engineer, Back-end
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Spain
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101000.00 - 131000.00 EUR / Year
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Affirm
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Ai Programmer
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Italy , Milan
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RaceWard Studio
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Senior OTT Software Engineer
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Provectus
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Technical Program Manager
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United States
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109700.00 - 135500.00 USD / Year
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Octave
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Cloud Senior Technical Product Manager
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United States , Bellevue; Overland Park
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130900.00 - 236200.00 USD / Year
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Product/Project Management Intern
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Bulgaria , Sofia
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Hewlett Packard Enterprise
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Discover Lead Analytics Engineer Jobs and step into a pivotal role at the intersection of data engineering, business intelligence, and strategic leadership. A Lead Analytics Engineer is a senior technical professional responsible for architecting, building, and maintaining the reliable data infrastructure that powers an organization's analytics and decision-making. This role goes beyond traditional data engineering by focusing on transforming raw data into clean, tested, and well-documented datasets, enabling self-service analytics and ensuring that business stakeholders have access to accurate, trustworthy information. Professionals in these jobs typically own the entire analytics data layer. Their core mission is to translate business needs into robust data models. Common responsibilities include designing and implementing scalable data transformation pipelines using modern tools like dbt, SQL, and cloud data platforms. They establish and enforce data modeling standards, write comprehensive data tests to guarantee quality, and create clear documentation to democratize data access. A significant part of the role involves collaborating with data analysts, scientists, and business leaders to understand requirements and ensure the data architecture supports evolving goals. Furthermore, Lead Analytics Engineers often serve as mentors, guiding junior team members and advocating for best practices in data engineering and analytics across the organization. To excel in Lead Analytics Engineer jobs, a specific blend of technical and soft skills is required. Technical expertise must include advanced proficiency in SQL and data modeling, along with deep hands-on experience with modern data stack tools such as dbt, cloud data warehouses (e.g., Snowflake, BigQuery, Redshift), and orchestration platforms. A strong understanding of software engineering best practices—like version control, testing, and modular code—is applied to the analytics codebase. Equally important are leadership and communication skills. Successful candidates possess the ability to translate complex technical concepts for non-technical audiences, bridge gaps between engineering and business teams, and drive data strategy. A process-driven mindset with a relentless focus on data accuracy, governance, and scalability is fundamental. If you are a data professional who thrives on building foundational data systems, enabling insights at scale, and leading data excellence, exploring Lead Analytics Engineer jobs could be your next career move. This career path is ideal for those who enjoy technical depth, strategic impact, and the challenge of turning data chaos into clarity, ultimately empowering entire organizations to make confident, data-driven decisions.

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