Data Engineer
4 weeks ago
Key Responsibilities
Lead the design and implementation of the data & analytics architecture ensuring
compliance, quality, and sustainable platform growth
Build scalable end-to-end data pipelines to integrate and model datasets from different
sources that meet functional and non-functional requirements
Manage the technical scope and architecture of the project before, during, and after
delivery
Work with product, business and functional stakeholders to understand data
requirements and downstream analytics needs
Responsible for ratifying technology solutions, producing concise Design documents,
contributing to work estimates
Translate business requirements & E2E designs into technical implementations based
on system capabilities
Define, and promote re-usable, extensible, scalable, and maintainable solutions
considering trade-off for vs benefit.
Communicate at all levels clearly and credibly about the importance of solution
design
Foster a data-driven culture throughout the team
Drive innovation through a good understanding of data, business drivers, and business
needs
Required Experience, Skills & Qualifications
Around 8-10 years of relevant experience working with High-Performance Data
Products or Data Systems as a Data Architect/Engineer.
Advanced level proficiency in Designing and Developing Data Products,
orchestration tools/services
Proficient with Software Engineering best practices, such as unit testing and
integration testing, and software development tools
Extensive experience in at least one cloud platform with Big Data services (EMR,
Databricks, etc.)
Relevant experience in databases (columnar, NoSQL, and MPP databases: Redshift,
Dynamodb, Aurora, Postgres, Google Bigquery and/or Snowflake) along with best
practices in portioning and clustering tables for efficient performance.
Should be aware of Security compliances and design practices
Exceptional interpersonal, analytical, and communication skills including the ability
to explain and discuss DevOps concepts with colleagues and teams
Fully adhere to and evangelize an entire CI/CD pipeline
Understands API development and use of JSON/XML as data formats
Knowledge and hands-on experience with data engineering tools for any cloud
provider. Eg. Apache Kafka, Apache Flink, Amazon S3 (Simple Storage Service),
AWS Glue, Amazon Athena, Presto, Apache Airflow, Apache Spark, Amazon EMR
(Elastic MapReduce), Amazon OpenSearch, Kibana, SQL (Structured Query
Language), JDBC (Java Database Connectivity), ODBC (Open Database
Connectivity), Parquet file format, Redshift, Trino, Airbyte
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