Responsibilities

As a Data Engineer, you will be a key member of the organization who will establish bridges between Infrastructure, Product Analytics, Business Technologies and GTM teams.

You will be responsible for conceptualizing, designing, and building data pipelines and services, while contributing towards the evolution of our data platform as a strategic asset for driving business decisions across the company.

How We Work

Workato Data Platform is based on the industry leading technologies such as Snowflake Data Warehouse, Clickhouse Database, Apache Kafka and AWS (lambda, fargate, etc ) and Workato SAAS solution itself for collecting data from third party business supporting services.

We are currently working on upgrading the platform to meet new requirements of rapidly growing business such as:

• Increase the confidence in analytical data.

• Make a clearest possible vision of the user journey.

• Minimize the gap between data emitting and data availability for analytical purposes.

We plan to achieve this by adopting some additional leading edge technologies such as DBT, Airflow, Kafka Streams, Kafka Connect and DataHub unified Metadata management Platform.

You will make a direct impact to this movement and together we will be able to push Workato data platform at least to the Level 4 of the maturity model.

How You’ll Make An Impact

• Hold strong ownership on data assets and pipelines that provide actionable insights into customer, product, GTM, and other key business functions.

• Design, implement and maintain scalable data pipelines and transformations using data from a variety of engineering and business systems.

• Ensure and sustain Data Quality, ensure analytical data artifacts are always accurate and maintain SLAs.

• Collaborate with Analysts, Product Owners and Stakeholders to fuel business intelligence with quality data models, increasing data accessibility and drive adoption of data.

Requirements

Qualifications / Experience / Technical Skills

• 5 years of work experience building & maintaining data pipelines on data-heavy environments (Data Engineering, Backend with emphasis on data processing, Data Science with emphasis on infrastructure)

• Fluent knowledge of  SQL.

• Strong knowledge of common analytical domain programming languages such as Python and Java.

• Ability to articulate a point of view and good communication, collaborative demeanor with work experience in distributed multinational teams.

• Deep background in data engineering and a proven track record of solving complex data problems and designing long-term solutions.

• Advanced English level.

Soft Skills / Personal Characteristics

• Experience with Data Pipeline Orchestration tools (Airflow, Dagster or similar).

• Experience with Data Warehousing Solutions (Snowflake, Redshift, BigQuery).

• Confidence in using Git, K8s and Terraform.

• Production experience with PostgreSQL and Clickhouse.

• Experience with building Data Quality checks and Data Integrity Rules.

• Experience with Data Streaming and CDC (Confluent platform,  Kafka Ecosystem, Apache Flink, Debezium).

• Knowledge of Python Data Libraries such as Pandas.

• Strong understanding of Data Privacy and Security (GDPR, CCPA).