Overview

Principal Data Engineer – Driving Data Excellence & BI Transformation At Siemens, we help organizations transform maintenance and operations through connected insights, AI-powered technology, and intelligent asset management solutions.

Responsibilities

• Leading the design, development, and optimization of large-scale data pipelines and data warehouse/lake solutions.
• Driving data integration (ETL/ELT) strategies and implementations across diverse platforms and technologies.
• Collaborating with BI and analytics teams to ensure data availability, quality, and accessibility for reporting and insights.
• Mentoring junior engineers and contributing to establishing best practices in data engineering and software development.
• Evaluating and implementing new data technologies and approaches to enhance our data ecosystem.
• Bachelor's degree in Engineering or Science, or equivalent practical experience.
• 10+ years of progressive experience in software and data engineering, with at least 8 years dedicated to data-centric roles.
• Mastery of programming languages such as Java, Scala, and/or Python, together with comprehensive knowledge of relational databases and advanced SQL skills to support analytical needs.
• Extensive cloud data experience, with in-depth expertise in AWS-based data services (e.g., Kinesis, Glue, RDS, Athena) and Snowflake Cloud Data Warehouse.
• Extensive experience designing and implementing data integration (ETL/ELT) solutions across diverse platforms using Java, Scala, Python, PySpark, and SparkSQL, with hands-on proficiency in ETL/ELT tools such as dbt and robust data pipeline orchestration skills.
• Demonstrated ability to build, optimize, and maintain production-grade data pipelines supporting batch, replication/CDC, and event streaming patterns for data lakes and warehouses.
• Skilled in data modeling, data migration strategies, and performance tuning for large-scale data systems.
• A proven history of contributing to or leading major initiatives involving the consolidation and rationalization of large-scale data environments, including complex data pipelines and internal/external partner integrations.
• Familiarity with the Software Development Life Cycle (SDLC), source control (e.g., Git), and best practices for ensuring data quality, with adherence to software engineering and Agile development best practices.
• Ability to align work schedule with the US Eastern Standard Time (EST) zone to ensure at least a 6-hour overlap with our US teams.
• Familiarity with leading BI tools such as Power BI and Apache Superset.
• Familiarity with AI-powered coding tools (e.g., GitHub Copilot, Claude Code) to enhance productivity and code quality.
• Hands-on experience with agentic development workflows, particularly Claude Code or an equivalent agent such as Snowflake CoCo, Cursor, GitHub Copilot agent mode, Windsurf, Codex CLI, or Gemini CLI — decomposing data engineering work into tasks an agent can execute end to end, steering and reviewing agent-generated code and tests, and applying sound judgment about where human review and design ownership remain essential.

Qualifications

• Practical use of Model Context Protocol (MCP) servers to give agents governed access to the data stack — for example the Snowflake MCP server (Cortex Search, Cortex Analyst, and Cortex Agent tooling) and the dbt MCP server (semantic layer queries, metadata and lineage discovery, and dbt operations such as compiling models and running tests) — along with the authentication and least-privilege considerations their service tokens require.
• A proactive and innovative approach to exploring and implementing cutting-edge technologies and solutions.
• Outstanding written and verbal communication skills, with the ability to articulate complex technical concepts to both technical and non-technical stakeholders.