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Senior Data Engineer

2 months ago

Bengaluru, India Talent500 Full time

Senior Data Engineer

JOB DUTIES:

  • Design and architect data lake solutions on AWS S3 and Snowflake, considering scalability, automation, security, and performance requirements.
  • Responsible for architecting, building, and optimizing data lake to store, process and analyze large volumes of structure and unstructured data.
  • Requirements gathering and analysis, development planning and co-ordination, data testing, documentation and ensuring high levels of data quality is maintained.
  • documentation.
  • Work with project leads, stakeholders, and business SMEs to define technical specifications to develop data integration requirements, code design and functional and technical documentation.
  • Work with specifications to design, develop, and maintain data infrastructure.
  • Work with cross functional teams and process owners on the development of test cases and scripts, test models and solutions to verify that requirements are met and ensuring high levels of data quality.
  • Promote and apply quality assurance best practices.
  • Understand and comply with data governance and compliance practices as defined for risk management. This includes data encryption practices, RBAC and security policies.
  • Promote and apply metadata management best practices supporting enterprise data catalogs.
  • Excellent problem-solving skills and ability to troubleshoot complex data engineering issues.
  • Benchmark application operational performance periodically, track (metrics) and fix issues.
  • Support change and release management processes.
  • Support incident and response management including problem solving and root cause analysis, documentation.
  • Support automation and on-call processes (Tier 1 / Tier 2).


SPECIFIC SKILLS OR OTHER REQUIREMENTS:

  • Requires 5 + years of experience with-
  • Primary Experience: Data Lakes and Data Integrations


Data Engineering:

  • AWS: 4+ years of required expertise with AWS services like S3, Glue, Lambda, EMR, Athena and proficiency in programming languages/ tools like Python, Scala, SQL.
  • Apache Software: 2+ years of required expertise in data processing with Spark & Flink, message broking using Kafka, orchestration using Airflow, processing high data volumes in open table formats like Iceberg.
  • Snowflake: 3+ years of required expertise with Snowflake SnowSQL, Snowpipe, (integrated with AWS S3), Streams and Tasks, Stored Procedures (expertise working with merge statements), Compute and Storage usage optimizations, Performance optimizations.
  • HVR (Fivetran): Near Real-time Data Replications with HVR is a plus.
  • SnapLogic: Data integrations with SnapLogic integration platform is a plus.
  • Certifications: AWS and Snowflake data engineering certifications is a plus.


Data Platforms:

  • Required: AWS S3 data lake store platform, Snowflake cloud data platform
  • Preferred: Denodo, SAP HANA


Programming/ Scripting:

  • Snowflake: SQL Scripting (Snowpipes, Tasks, Streams, Merge Statements, Stored Procedures, Functions, Security Policies – DDM, Row-Access), SQL Scripting, Python
  • AWS: Apache spark, Python


Data Orchestration:

  • Preferred: Control-M, Apache Airflow


Cloud Storage Platforms:

  • Required: Amazon Web Services
  • Preferred: Microsoft Azure


Sr Data Engineer

  • Required: Knowledge and experience integrating data from SAP ERP (On premises, Cloud), Salesforce CRM, Workday, Planisware, Team Center PLM, Relational databases, REST APIs
  • Preferred: ServiceNow, MFG MES, IOT/ Sensor data platforms, Unstructured data, Other SaaS Applications


Data Operations: Excellent understanding of Data Ops practices for data management.


Stakeholder Engagement: Ability to take the lead and drive project activities collaborating with Analytics stakeholders and ensure the requirement is completed.

Solution Design: Good understanding of end-to-end solution architecture and design practices, ability to document solutions (maintain diagrams)


Governance: Good understanding of working with companies having regulated systems and processes for data. Adherence to data protection practices using tagging, security policies and data security (object-level, column-level, row-level).


Data Warehousing: Good understand on how data onboarded in the data lakes is used to support data warehouses. Impact of risks and change to a data warehouse. Fundamental concepts of dimensional modeling, experience working on data warehouse solutions, requirement gathering, design & build.


Data As-A-Product: Preferred knowledge and experience working with data treated as data products. is following a hybrid data mesh architecture which promotes data as a product for data lifecycle management.


Operating Systems: Windows, Linux


EDUCATION & EXPERIENCE:

Bachelor’s degree equivalent in Computer Science/Engineering or equivalent degree.