Data Engineer

Data Engineer

Desired Experience Range:

Location of Requirement:

TCS has always been in the spotlight for being adept in the next big technologies”. What we can offer you is a space to explore varied technologies and quench your techie soul.

Must-Have:

• 3+ years of relevant data engineering experience.
• Good understanding of data modelling principles including Dimensional modelling and data normalization principles.
• 2+ years of work experience implementing and reporting on business key performance indicators in data warehousing environments, exposure to reliability engineering best practices a plus.
• 2+ years of experience using analytic SQL, working with traditional relational databases and/or distributed systems (Snowflake or Redshift), required.
• 1+ years of experience programming languages (e.g. Python, PySpark), preferred.

• Experience with Snowflake, Databricks/EMR/Spark, and/or Airflow.

• Experience with process engineering and continuous improvement.
• Detailed problem-solving approach, coupled with a strong sense of drive and ownership.
• Strong communication skills – written and verbal presentations.
• Comfortable working in a fast-paced and highly collaborative environment.
• Familiarity with Agile Scrum principles and ceremonies

Key Responsibilities

• You will assist with on-call rotations as an Incident Commander to help categorize and track real time data incidents, loop in engineering resources as needed to ensure a timely resolution.
• You will help drive our problem management process to ensure root causes of data incidents are analysed and understood, and follow-up work is prioritized with our partner teams.
• You will play a key role in our long-term Incident and Problem Management strategy by contributing to the development of a Data Reliability Observability Platform that will assist in quantifying incident impacts, automate issue detection and track the overall health of our data and data pipelines.
• You will partner with our support and data quality teams, defining success KPI’s, ensuring observability and automation coverage of our pipelines and help create dashboards and visualizations for teams to monitor the overall health of our data