Data Engineer

We’re looking for a Data Engineer with data science experience who has deep expertise building and maintaining large-scale data pipelines for time series datasets using Databricks or comparable big data platforms (e.g., Spark-based or cloud-native analytics frameworks). The role is engineering‑first, focused on designing scalable, reliable data architecture and pipelines that support high‑quality analytics and enable the easy integration of AI and machine learning workloads. Applied data science and machine learning will be used as needed to support product features and insights. You’ll partner closely with product and engineering teams to turn complex data into trusted, production‑ready data assets.Key ResponsibilitiesDevelop scalable data pipelines using Databricks, PySpark, and cloud-native tools to ingest, clean, and transform large time series datasets.Design and maintain data models optimized for analytical and machine learning workloads.Build, train, and evaluate machine learning models, especially those suited for forecasting, anomaly detection, and pattern recognition in time series data.Collaborate with cross-functional teams to understand data needs, define requirements, and deliver high-quality analytical solutions.Optimize Spark jobs for performance, reliability, and cost efficiency.Implement best practices for data quality, versioning, testing, and documentation.Explore new tools and techniques to improve data processing, modeling, and automation.Required Qualifications3+ years of experience in data engineering, data science, or a hybrid role.Strong proficiency with Databricks (or comparable big data platforms), PySpark, and distributed data processing.Hands-on experience with time series data, including feature engineering, forecasting, and anomaly detection.Solid programming skills in Python and familiarity with common data science libraries (pandas, NumPy, scikit-learn, etc.).Experience building and maintaining ETL/ELT pipelines in a cloud environment (Azure, AWS, or GCP).Understanding of machine learning fundamentals and experience applying models to real-world datasets.Strong SQL skills and experience working with large relational or NoSQL databases.Preferred QualificationsExperience with MLflow, Delta Lake, or Databricks AutoML.Familiarity with streaming data (Structured Streaming, Kafka, Kinesis, etc.).Background in MLOps, CI/CD for data pipelines, or productionizing ML models.Experience with data visualization tools (Power BI, Tableau, etc.).Knowledge of statistics, experimental design, or causal inference.Soft SkillsAbility to translate complex technical concepts into clear business insights.Strong communication and collaboration skills.Curiosity, ownership mindset, and a willingness to explore new approaches.Comfort working in a fast-paced, iterative environment.What We OfferOpportunity to shape data strategy and architecture for high-impact analytical products.A collaborative team that values experimentation, learning, and technical excellence.Competitive compensation, benefits, and professional development support.