Data Integration Engineer Lead Consultant

Tasks

Design and implement data processing systems using distributed frameworks like Hadoop Spark Snowflake Airflow or other similar technologies. This involves writing efficient and scalable code to process transform and clean large volumes of structured and unstructured data.

Build data pipelines to ingest data from various sources such as databases APIs or streaming platforms.

Integrate and transform data to ensure its compatibility with the target data model or format

Design and optimize data storage architectures including data lakes data warehouses or distributed file systems.

Implement techniques like partitioning compression or indexing to optimize data storage and retrieval. Identifying and resolving bottlenecks tuning queries and implementing caching strategies to enhance data retrieval speed and overall system efficiency.

Design and implement data models that support efficient data storage retrieval and analysis.

Collaborate with data scientists and analysts to understand their requirements and provide them with wellstructured and optimized data for analysis and modeling purposes.

Utilize frameworks like Hadoop or Spark to perform distributed computing tasks such as parallel processing distributed data processing or machine learning algorithms

Implement security measures to protect sensitive data and ensure compliance with data privacy regulations.

Establish data governance practices to maintain data integrity quality and consistency.

Identify and resolve issues related to data processing storage or infrastructure.
Monitor system performance identify anomalies and conduct root cause analysis to ensure smooth and uninterrupted data operations.

Collaborate with crossfunctional teams including data scientists analysts and business stakeholders to understand their requirements and provide technical solutions.

Communicate complex technical concepts to nontechnical stakeholders clearly and concisely.

Stay updated with emerging technologies tools and techniques in the field of big data engineering.

Explore and recommend new technologies to enhance data processing storage and analysis capabilities.

Train and mentor junior data engineers providing guidance and knowledge transfer.

Requirements

A bachelors or masters degree in Computer Science Information Systems or a related field is typically required.

Work commercial experience as a Data Engineer or a similar role.

Proficiency in programming languages such as Python R or Scala is essential.

Indepth knowledge and experience with distributed systems and technologies including Apache Hadoop Spark Hive or similar frameworks.

Familiarity with cloudbased platforms like AWS Azure or Google Cloud is highly desirable.

Solid understanding of data processing techniques such as batch processing realtime streaming and data integration.

Proficiency in working with relational and nonrelational databases such as MSSQL MySQL PostgreSQL or Cassandra.

Knowledge of data warehousing concepts and technologies like Redshift Snowflake or BigQuery is beneficial.

Benefits

Office as an option model. You can choose to work remotely or in the office.

Flexibility regarding working hours.

Upskilling support. Capability development programs Competency Centers knowledge sharing sessions community webinars 110 training opportunities yearly.

A bachelor's or master's degree in Computer Science, Information Systems, or a related field is typically required. Work commercial experience as a Data Engineer or a similar role. Proficiency in programming languages such as Python, R, or Scala is essential. In-depth knowledge and experience with distributed systems and technologies, including Apache Hadoop, Spark, Hive or similar frameworks. Familiarity with cloud-based platforms like AWS, Azure, or Google Cloud is highly desirable. Solid understanding of data processing techniques such as batch processing, real-time streaming, and data integration. Proficiency in working with relational and non-relational databases such as MSSQL, MySQL, PostgreSQL, or Cassandra. Knowledge of data warehousing concepts and technologies like Redshift, Snowflake, or BigQuery is beneficial.