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Senior Data and AI Engineer with 16+ years of experience across data engineering, analytics, and AI/ML. Proven track record in building cloud-native data platforms, implementing GenAI solutions, LLMs, and enterprise-scale RAG pipelines. Expert in Snowflake, AWS, and BI tools like Looker and Tableau. Adept at MLOps, DBT, and AI DevOps for deploying scalable, secure, and intelligent systems. Strong leadership in AI projects, enabling business transformation through automation, data-driven decision-making, and next-gen GenAI applications.
I'm a senior data engineer with a strong track record of designing and optimizing data infrastructure to unlock business value. With deep expertise in Python, SQL, Airflow, and AWS, I’ve led impactful projects—from building ML-powered job classification systems to boosting CRM engagement by 197% and cutting pipeline runtimes by 800%. I thrive in cross-functional environments, bringing analytical rigor, clean engineering practices, and a focus on outcomes. My experience spans startups to established platforms, and I’m passionate about solving complex data challenges that drive real results.
I am a data-driven builder with over five years of work in data, AI, and cloud tech. I turn raw input into models, tools, and clear insight for teams and clients. I have strong skill in Python, SQL, and Power BI. I have worked with cloud apps, APIs, and large AI tools to solve real goals in fast setups. I ask the right questions, move with care, and shape each task to fit the need. My focus is always on value, clarity, and trust in the result.
I am a results-driven Data Engineer with over 3 years of experience in building scalable ETL/ELT pipelines, optimizing data workflows, and ensuring data accuracy. Proficient in Python, SQL, and big data tools like Spark and Hive, I specialize in designing and implementing cloud-based data systems using AWS and data orchestration using Apache Airflow/Autosys. My expertise includes data modeling, query optimization, and stream processing. I thrive in collaborative, agile environments and am committed to delivering high-quality, reliable data solutions to support data-driven decision-making.
At Techamana, we maintain the highest standards in developer selection. Our Data Warehousing experts undergo a comprehensive 4-step vetting process that evaluates technical skills, problem-solving abilities, communication skills, and team and culture fit. Only the top 5% of applicants pass our rigorous screening, ensuring you work with exceptional talent who can deliver outstanding results for your projects.
Rigorous coding challenges and problem-solving tests to evaluate Data Warehousing proficiency and best practices.
Thorough examination of past projects and contributions to open-source Data Warehousing repositories to assess real- world experience.
Video responses for open-ended questions to assess problem-solving ability, communication skills, and team and culture fitment.
When hiring a Data Warehousing expert, seek star/snowflake schema design and OLAP cube development skills.
Candidates should optimize queries with partitioning/indexing, schedule incremental loads, and work with Redshift, Snowflake, or BigQuery for BI platforms.
Industry insights and best practices
Data warehousing involves storing and managing large amounts of data for analysis and reporting. Modern data warehouses utilize cloud-based platforms and advanced analytics tools.
Data warehouses provide data for business intelligence, enabling data-driven decision-making.
Data warehouses are used to generate reports and perform data analysis to gain insights from data.
Data mining techniques can be applied to data warehouses to discover hidden patterns and relationships.
Data warehouses provide data for predictive modeling, allowing businesses to forecast future trends.
Majority of our clients choose to continue working with our talent after their initial project
Our screening and matching process ensures exceptional talent are matched to your precise needs.
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