DataSMBEnterprise

Data Engineer

We are hiring a Data Engineer to design, build, and maintain the data infrastructure that powers analytics, machine learning, and business intelligence across [Company Name]. You will own data pipelines from ingestion to consumption, ensure data quality and reliability, and collaborate with analysts, data scientists, and product teams to make data accessible and trustworthy.

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Key Responsibilities

  • Design and implement scalable data pipelines that ingest, transform, and load data from diverse sources (APIs, databases, event streams, third-party SaaS tools)
  • Build and maintain a modern data warehouse or lakehouse architecture using tools like Snowflake, BigQuery, Databricks, or Redshift
  • Develop and enforce data quality checks, monitoring, and alerting to ensure pipeline reliability and data accuracy
  • Optimize query performance and storage costs in the data warehouse through partitioning, clustering, and materialization strategies
  • Collaborate with data analysts and data scientists to model data for downstream consumption (star schemas, OBT, metrics layers)
  • Manage orchestration workflows using tools like Airflow, Dagster, or Prefect to schedule and monitor data jobs
  • Implement data governance practices including cataloging, lineage tracking, access controls, and PII handling

Required Skills & Experience

  • 3+ years of data engineering experience building production data pipelines
  • Strong SQL skills including window functions, CTEs, and query optimization
  • Proficiency in Python for data processing, scripting, and pipeline development
  • Experience with at least one modern data warehouse (Snowflake, BigQuery, Databricks, or Redshift)
  • Hands-on experience with an orchestration tool (Airflow, Dagster, or Prefect)
  • Familiarity with data modeling concepts (dimensional modeling, star schema, data vault)
  • Understanding of cloud platforms (AWS, GCP, or Azure) and their data-related services (S3, GCS, Glue, Dataflow)
  • Experience with version control for data (dbt, Git-based workflows)

Nice-to-Have

  • Experience with dbt for data transformation and testing
  • Familiarity with streaming data tools (Kafka, Kinesis, Flink, or Spark Streaming)
  • Knowledge of data cataloging and lineage tools (Atlan, DataHub, or OpenMetadata)
  • Experience with infrastructure-as-code for data infrastructure (Terraform, Pulumi)
  • Exposure to analytics engineering or metrics layer tools (MetricFlow, Cube)

Tech Stack

PythonSQLSnowflakedbtAirflowSparkKafkaAWS S3TerraformGitDockerBigQuery

What We Offer

  • Competitive salary and equity at [Company Name]
  • Opportunity to build the data platform from the ground up or scale an existing one
  • Annual conference and training budget (Data Council, Coalesce, etc.)
  • Comprehensive health, dental, and vision insurance
  • Flexible remote work with async-first communication culture
  • Collaborative team that values data quality and engineering best practices

Interview Process

  1. 1Recruiter phone screen (30 min) — background, experience overview, and logistics
  2. 2Technical screen (45 min) — SQL problem-solving and data modeling discussion
  3. 3Data pipeline design interview (60 min) — design a pipeline for a realistic business scenario, discuss trade-offs
  4. 4Coding interview (45 min) — Python-based data processing problem
  5. 5Hiring manager conversation (45 min) — team dynamics, career growth, and mutual fit
  6. 6Reference checks and offer

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