Senior Equities Data Engineer

ROLE OVERVIEW

The Equities Alternative Data team is responsible for sourcing, integrating, curating, and delivering differentiated alternative datasets that support BAM's Equities Long/Short investment teams. We build and operate the data products, platforms, and workflows that enable portfolio managers, analysts, and quantitative researchers to efficiently discover, evaluate, back-test, and consume high-quality alternative data signals.

We are looking for an experienced, pragmatic, and eager data engineer to join the team. This individual will help design and scale the data infrastructure underpinning our alternative-data offering-from vendor onboarding and ingestion through normalization, entity mapping, quality controls, historical backfills, and researcher-facing delivery.

Key responsibilities:
Design, build, and grow the data platform and pipelines that support alternative-data research and production use cases for Equities L/S teams
Own end-to-end dataset onboarding, including ingestion, schema design, normalization, historical backfills, metadata, lineage, and access patterns
Build scalable, reliable ETL/ELT workflows for large, diverse, and frequently changing datasets, including structured, semi-structured, and unstructured sources
Leverage and extend BAM's in-house data-quality framework to monitor, reconcile, and alert on the timeliness, completeness, accuracy, and continuity of critical alternative datasets
Partner closely with portfolio managers, fundamental analysts, quantitative researchers, and data-product stakeholders to translate investment use cases into robust technical solutions
Design researcher-friendly data access and consumption patterns, including APIs, curated tables, data extracts, and integrations with research and back-testing environments
Collaborate with external data vendors and internal technology partners to evaluate feeds, resolve data issues, and improve operational reliability
Contribute to standards for dataset documentation, discoverability, governance, entitlement controls, and auditability
Balance feasibility, stability, scalability, data quality, and time-to-market when delivering solutions in a fast-paced investment environment
Participate in a global engineering team, contributing to code reviews, architecture discussions, operational support, and shared platform improvements

QUALIFICATIONS & REQUIREMENTS:
5+ years of experience in a data-intensive engineering role, building and operating data pipelines, platforms, or data products
Demonstrable expertise in SQL and databases, particularly time-series, analytical, and large-scale data stores
Strong programming skills in Python and/or Rust/C++, plus experience with data-manipulation libraries or frameworks such as Pandas, Polars, PySpark, Dask, or similar
Experience architecting and building scalable ETL/ELT pipelines, including batch processing and, ideally, streaming or near-real-time workflows
Experience with column-oriented storage and serialization formats such as Parquet and Arrow
Strong debugging skills across the application and data stack, with demonstrated problem-solving ability in production environments
Experience with code optimization and performance tuning for large-scale data processing workloads
Familiarity with distributed-system data components, such as Kafka, Redis, workflow orchestration tools, messaging systems, or caching layers
Proven ability to work directly with technical and non-technical stakeholders and communicate trade-offs clearly
Strong ownership mindset, attention to data quality, and ability to deliver in an iterative, business-focused environment

Additional experience in the following areas is a plus:
Experience working with alternative data, including consumer transactions, web data, geolocation, app data, job postings, supply-chain data, sentiment, or other nontraditional datasets
Familiarity with financial instruments, public-equity research workflows, equity factor research, portfolio management, risk management, or back-testing environments
Experience with security master, entity resolution, issuer/ticker mapping, point-in-time datasets, corporate actions, or historical identifier management
Prior experience with ClickHouse, Snowflake, Databricks, Trino, DuckDB, or similar analytical data platforms
Experience building APIs or application-level services to expose data products and business logic
Experience with orchestration and transformation tooling such as Airflow, Dagster, dbt, or similar
Familiarity with observability tooling such as Prometheus, Grafana, Sentry, OpenTelemetry, or distributed tracing and monitoring frameworks
Experience with distributed stateful stream processing, such as Kafka Streams, Flink, or Arroyo
Experience with cloud object storage and compute environments, including data access controls, cost management, and production operational practices