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Senior Data Engineer at ODC - SmartMessage

Remote: 
Full Remote
Contract: 
Experience: 
Senior (5-10 years)
Work from: 

Offer summary

Qualifications:

Bachelor’s or Master’s degree in Computer Science, 5+ years of experience in data engineering, Proficiency in noSQL and graph databases, Experience with cloud platforms (AWS, GCP, Azure), Strong experience with Hadoop and Spark.

Key responsabilities:

  • Design, build, and maintain scalable data pipelines
  • Implement data lake solutions on cloud platforms
  • Ensure data quality, integrity, and security
  • Collaborate with data scientists for machine learning initiatives
  • Continuously monitor and optimize data pipelines
SmartMessage logo
SmartMessage SME https://smartmessage.com/
51 - 200 Employees
See more SmartMessage offers

Job description

Who are we?

We are a globally expanding software technology company that helps brands communicate more effectively with their audiences. We are looking forward to expand our people capabilities and success in developing high-end solutions beyond existing boundaries and establish our brand as a Global Powerhouse.

We are free to work from wherever we want and go to the office whenever we like!!!

What is the role?

We are looking for a highly skilled and motivated Senior Data Engineer to join our dynamic team. The ideal candidate will have extensive experience in building and managing data pipelines, noSQL databases, and cloud-based data platforms. You will work closely with data scientists and other engineers to design and implement scalable data solutions.

Key Responsibilities:

  • Design, build, and maintain scalable data pipelines and architectures.
  • Implement data lake solutions on cloud platforms.
  • Develop and manage noSQL databases (e.g., MongoDB, Cassandra).
  • Work with graph databases (e.g., Neo4j) and big data technologies (e.g., Hadoop, Spark).
  • Utilize cloud services (e.g., S3, Redshift, Lambda, Kinesis, EMR, SQS, SNS).
  • Ensure data quality, integrity, and security.
  • Collaborate with data scientists to support machine learning and AI initiatives.
  • Optimize and tune data processing workflows for performance and scalability.
  • Stay up-to-date with the latest data engineering trends and technologies.

Detailed Responsibilities and Skills:

  • Business Objectives and Requirements:
    • Engage with business IT and data science teams to understand their needs and expectations from the data lake.
    • Define real-time analytics use cases and expected outcomes.
    • Establish data governance policies for data access, usage, and quality maintenance.
  • Technology Stack:
    • Real-time data ingestion using Apache Kafka or Amazon Kinesis.
    • Scalable storage solutions such as Amazon S3, Google Cloud Storage, or Hadoop Distributed File System (HDFS).
    • Real-time data processing using Apache Spark or Apache Flink.
    • NoSQL databases like Cassandra or MongoDB, and specialized time-series databases like InfluxDB.
  • Data Ingestion and Integration:
    • Set up data producers for real-time data streams.
    • Integrate batch data processes to merge with real-time data for comprehensive analytics.
    • Implement data quality checks during ingestion.
  • Data Processing and Management:
    • Utilize Spark Streaming or Flink for real-time data processing.
    • Enrich clickstream data by integrating with other data sources.
    • Organize data into partitions based on time or user attributes.
  • Data Lake Storage and Architecture:
    • Implement a multi-layered storage approach (raw, processed, and aggregated layers).
    • Use metadata repositories to manage data schemas and track data lineage.
  • Security and Compliance:
    • Implement fine-grained access controls.
    • Encrypt data in transit and at rest.
    • Maintain logs of data access and changes for compliance.
  • Monitoring and Maintenance:
    • Continuously monitor the performance of data pipelines.
    • Implement robust error handling and recovery mechanisms.
    • Monitor and optimize costs associated with storage and processing.
  • Continuous Improvement and Scalability:
    • Establish feedback mechanisms to improve data applications.
    • Design the architecture to scale horizontally.

Qualifications:

  • Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
  • 5+ years of experience in data engineering or related roles.
  • Proficiency in noSQL databases (e.g., MongoDB, Cassandra) and graph databases (e.g., Neo4j).
  • Strong experience with cloud platforms (e.g., AWS, GCP, Azure).
  • Hands-on experience with big data technologies (e.g., Hadoop, Spark).
  • Proficiency in Python and data processing frameworks.
  • Experience with Kafka, ClickHouse, Redshift.
  • Knowledge of ETL processes and data integration.
  • Familiarity with AI, ML algorithms, and neural networks.
  • Strong problem-solving skills and attention to detail.
  • Excellent communication and teamwork skills.
  • Entrepreneurial spirit and a passion for continuous learning.

Join our team!

Required profile

Experience

Level of experience: Senior (5-10 years)
Spoken language(s):
English
Check out the description to know which languages are mandatory.

Other Skills

  • Problem Solving
  • Detail Oriented
  • Teamwork
  • Verbal Communication Skills

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