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Manager/Associate Director - Project Manager(Financial Services)

posted by: spj_bot

Tiger Analytics is an advanced analytics consulting firm. We are the trusted analytics partner for several Fortune 100 companies, enabling them to generate business value from data. Our consultants bring deep expertise in Data Science, Machine Learning, and AI. Our business value and leadership have been recognized by various market research firms, including Forrester and Gartner.

We are looking for an experienced Project Manager – Data Engineering to lead and deliver large-scale data transformation and migration initiatives across Talend, Databricks, and cloud data ecosystems. The role demands a strong blend of technical depth and project delivery leadership, working closely with onshore and offshore teams to ensure timely, high-quality delivery across complex data programs.

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Data Engineer

posted by: spj_bot

InfyStrat is looking for a proficient Data Engineer to strengthen our data team. In this role, you will be integral in designing and implementing data pipelines that facilitate the efficient extraction, transformation, and loading (ETL) of data across various platforms. You will collaborate with data analysts, scientists, and business units to provide reliable and accurate datasets to drive decision-making processes. The ideal candidate should possess a combination of technical proficiency and creativity to solve complex data challenges. We foster a culture of innovation at InfyStrat, where the contributions of our team members are essential to transforming our data into valuable insights. Join us to help build data solutions that empower our business growth.

Key Responsibilities

  • Design, implement, and manage ETL processes to collect and transform data from diverse sources.
  • Develop and maintain data models, ensuring they meet business needs and performance requirements.
  • Optimize database performance and troubleshoot data-related issues.
  • Collaborate with stakeholders to identify data needs and develop solutions accordingly.
  • Implement data quality monitoring and validation to maintain data integrity.
  • Keep up with industry trends and emerging technologies to continually enhance data engineering practices.

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