Data Engineer (Remote)

Location: Remote

Employment Type: Permanent role

Client: Bilge Adam(UK Based client)

Work Time: 2pm-11pm

Experience: 4-6years

Job Description

The Role

As a Data Engineer you’d be working with us to design, maintain, and improve various analytical and operational services and infrastructure which are critical for many other functions within the organization. These include the data lake, operational databases, data pipelines, large-scale batch and real-time data processing systems, a metadata and lineage repository, which all work in concert to provide the company with accurate, timely, and actionable metrics and insights to grow and improve our business using data. You may be collaborating with our data science team to design and implement processes to structure our data schemas and design data models, working with our product teams to integrate new data sources, or pairing with other data engineers to bring to fruition cutting-edge technologies in the data space.

Our Ideal Candidate

We expect candidates to have in-depth experience in some of the following skills and technologies and be motivated to build up experience and fill any gaps in knowledge on the job. More importantly, we seek people who are highly logical, with a balance of respect for best practices and using their own critical thinking, adaptable to new situations, capable of working independently to deliver projects end-to-end, communicates well in English, collaborates effectively with teammates and stakeholders, and eager to be on a high-performing team, taking their careers to the next level with us.

Highly Relevant

• General computing concepts and expertise: Unix environments, networking, distributed and cloud computing
• Python frameworks and tools: pip, pytest, boto3, pyspark, pylint, pandas, scikit-learn, keras
• Agile/Lean project methodologies and rituals: Scrum, Kanban
• Workflow scheduling and monitoring tools: Apache Airflow, Luigi, AWS Batch
• Columnar and big data databases: Athena, Redshift, Vertica, Snowflake, Hive/Hadoop
• Version control: git commands, branching strategies, collaboration etiquette, documentation best practices
• General AWS or Cloud services: Glue, EMR, EC2, ELB, EFS, S3, Lambda, API Gateway, IAM, Cloudwatch, DMS
• Container management and orchestration: Docker, Docker Swarm, ECS, EKS/Kubernetes, Mesos
• CI / CD tools: CircleCI, Jenkins, TravisCI, Spinnaker, AWS CodePipeline .

Also Good To Have

• JVM languages, frameworks and tools: Kotlin, Java, Scala / Maven, Spring, Lombok, Spark, JDK Mission Control
• Distributed messaging and event streaming systems: Kafka, Pulsar, RabbitMQ, Google Pub/Sub
• RDBMS and NoSQL databases: MySQL, PostgreSQL / DynamoDB, Redis, Hbase
• Enterprise BI tools: Tableau, Qlik, Looker, Superset, PowerBI, Quicksight
• Streaming data processing frameworks: Spark Streaming, Apache Beam, Apache Flink
• Data science environments: AWS Sagemaker, Project Jupyter, Databricks
• Log ingestion and monitoring: ELK stack (Elasticsearch, Logstash, Kibana), Datadog, Prometheus, Grafana
• Metadata catalogue and lineage systems: Amundsen, Databook, Apache Atlas, Alation, uMetric
• Data privacy and security tools and concepts: Tokenization, Hashing and encryption algorithms, Apache Ranger

Skills: data lake,python,microservices,kafka,pyspark,data landscape