• Building state of the art pipelines and data models that are at the heart of the studio decision-making process. • Working on large-scale lakehouse and warehouse analytics systems that process data feeds in real-time and batch processes.

Role Description This is a full-time, high-impact position for a Senior Data Engineer with expertise in Databricks, dbt, and Apache Airflow to support a critical CRM data architecture migration for a key client in the Life Sciences industry. - Join an urgent initiative to backfill key engineering capabilities and maintain momentum during an ongoing CRM system transition. - Migrate enterprise customer data from Veeva CRM to Salesforce Life Sciences Cloud, integrated with an underlying AWS S3 cloud environment and Databricks data warehouse. - Build out, configure, and redirect data ingestion pipelines out of Life Sciences Cloud into the data warehouse. - Implement dbt models and Airflow orchestrations to ensure complete data accuracy. Candidates must be able to operate strictly on US East Coast business hours (location is flexible across North America, LATAM, or remote with full Eastern Time overlap). Qualifications - 5+ years of hands-on data engineering experience with deep expertise in AWS, Databricks, dbt, and Apache Airflow. - Strong programming proficiency in Python/PySpark and Advanced SQL (complex joins, analytical window functions). - Hands-on expertise in Databricks platform architecture, Lakehouse implementation, Delta Lake, Unity Catalog, and cluster performance tuning. - Proven track record of architecting and executing migrations. - Demonstrated experience scaling platform performance. - Proven experience building scalable transformations pipelines using dbt for data transformation, testing, and documentation. - Solid background orchestrating complex workflow DAGs with Apache Airflow. - Experience working with AWS cloud infrastructure, specifically AWS S3 as an underlying data lake storage layer. - Hands-on experience developing integrations and data ingestion pipelines for CRM platforms, specifically Salesforce, Salesforce Life Sciences Cloud, and/or Veeva CRM. - Understanding of data structures, customer master data, and analytics workflows within the Life Sciences. - Deep understanding of SDLC/Agile and DevOps for CI/CD and artifact management. - Knowledge of AWS and Databricks security best practices and compliance standards. - Certifications Preferred: Databricks Certified Data Engineer Associate/Professional, Databricks Spark Developer, and major cloud certifications in AWS. Requirements - Ability to maintain 100% full working time overlap with US East Coast business hours (ET). - Flexible location (US, Canada, LATAM, or remote ET). Responsibilities - Architect, build, and deploy data integration pipelines connecting Salesforce Life Sciences Cloud to the client’s Databricks warehouse environment. - Execute pipeline modifications to transition legacy data feeds from Veeva CRM to Salesforce Life Sciences Cloud, updating warehouse models accordingly. - Write clean, modular dbt transformation models and organize end-to-end DAG execution using Apache Airflow. - Manage Delta tables and optimize Databricks clusters and AWS S3 storage for high performance and cost efficiency. - Implement data quality testing, schemas, and verification rules in dbt and Python to guarantee accurate data delivery. - Build and enforce proactive monitoring frameworks. - Work closely with project leads, solution architects, and technical stakeholders during US East Coast hours to ensure rapid iteration and goal completion. Equal Opportunity Employer Race, Color, Religion, Sex, Sexual Orientation, Gender Identity, National Origin, Age, Genetic Information, Disability, Protected Veteran Status, or any other legally protected group status.

Role Description We're seeking an experienced Data Engineer/ Sr Data Engineer / Lead Data Engineer with expertise in data engineering across major data platforms. The ideal candidate will have a strong background in Python, SQL, ETL, and data modeling, with experience in tools like Teradata, Informatica, Hadoop, Spark, PySpark, ADF, Snowflake, and Big Data. Cloud knowledge (AWS, Azure, or GCP) is a plus. The role requires a willingness to transition and upskill into Databricks & AI/ML projects. - Design, develop, and maintain large-scale data systems - Develop and implement ETL processes using various tools and technologies - Collaborate with cross-functional teams to design and implement data models - Work with big data tools like Hadoop, Spark, PySpark, and Kafka - Develop scalable and efficient data pipelines - Troubleshoot data-related issues and optimize data systems - Transition and upskill into Databricks & AI/ML projects Qualifications - Relevant years of experience in data engineering - Strong proficiency in Python, SQL, ETL, and data modeling - Experience with one or more of the following: - Teradata - Informatica - Hadoop - Spark - PySpark - ADF - Snowflake - Big Data - Scala - Kafka - Cloud knowledge (AWS, Azure, or GCP) is a plus - Willingness to learn and adapt to new technologies, specifically Databricks & AI/ML Requirements - Experience with Databricks - Knowledge of AI/ML concepts and tools - Certification in relevant technologies Benefits - Competitive salary and benefits - Opportunity to work on cutting-edge projects - Collaborative and dynamic work environment - Professional growth and development opportunities - Remote work opportunities & flexible hours

Role Description Location: Brazil Job Mode: Full-time Work Mode: Work from home Essential Skills: - Desire to work at high level with stakeholders to devise, understand and communicate clearly requirements and architecture design for data platforms in an efficient manner. - Large Experience with architecture, governance, security, design, business mapping and understanding, performance and tuning for data lake, data warehouse and other data storage systems and transformation. - Experience with talking with customers and stakeholders and extracting business and technical requirements in high and low level. - Strong knowledge of Data frameworks: Hadoop (YARN, HDFS), Hive, Spark, Kafka, Pentaho, Airflow, AWS data tools. - Experience manipulating data using SQL, NoSQL and unstructured data sources, including metadata. - Strong knowledge of ETL frameworks and workflow processes (data ingestion, clean up and preparation). Highly Desirable Skills: - Experience with Python and Java for system administration. - Large experience with data modeling and data design. - System administration experience on Linux platforms. - Financial and/or banking marketing knowledge is a plus. - Experience with AWS platform. Additional Skills: - Machine learning algorithms and workflow. - Pipeline and workflow orchestration (Oozie, Luigi). - Experience with Kubernetes. Company Description Encora is the preferred digital engineering and modernization partner of some of the world’s leading enterprises and digital native companies. With over 9,000 experts in 47+ offices and innovation labs worldwide, Encora’s technology practices include: - Product Engineering & Development - Cloud Services - Quality Engineering - DevSecOps - Data & Analytics - Digital Experience - Cybersecurity - AI & LLM Engineering At Encora, we hire professionals based solely on their skills and qualifications, and do not discriminate based on age, disability, religion, gender, sexual orientation, socioeconomic status, or nationality.